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Angular2App-LoginRegistration | angular2app loginregistration this is a sample application which contains login and registration forms this application built with angular 2 and typescript this sample uses a fake backend that stores users in html5 local storage you can run this sample app using angular cli by following below steps step 1 set up the development environment you need to set up your development environment before you can do anything install node js and npm if they are not already on your machine install angular cli through command line interface install npm install g angular cli step 2 create a new project generate a new project and skeleton application by running the following commands ng new application name step3 run the application go to the project directory and launch the server cd application name ng serve open here ng serve command launches the server watches your files and rebuilds the app as you make changes to those files using the open or just o option will automatically open your browser on http localhost 4200 step4 open the component file eg app component html app component css etc under path src app and change any content or styles to see the changes on browser immediately without refresh the page manually | server |
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ele-web | ele web this is the web client for ele https ele io it s built using polymer and hosted on and developed by divshot http www divshot com ele is an in browser editor for web components local development ele is separated into two pieces the static front end web client this repo and ele api https github com divshot ele api the node js back end you ll need both repositiories node js and mongodb to get local development up and running cloning mkdir ele cd ele git clone https github com divshot ele web git git clone https github com divshot ele api git api up and running you should follow the instructions in the api readme https github com divshot ele api to get it up and running once that s done head back here to get the web client properly set up running the web client divshot allows for client side environment variables to develop locally you ll need to create a env json file that looks like this json api origin http url of your api server where api origin is set to whatever host and port on which you re running the node js api for instance http localhost 3000 then you just need to install the bower components and you re up and running bower install npm install g divshot cli divshot server p 4000 that s it you should now be able to visit http localhost 4000 and see the ele landing page | front_end |
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gio-design | growingio design react ui growingio commitizen friendly https img shields io badge commitizen friendly brightgreen svg https commitizen github io cz cli continuous deployment https github com growingio gio design workflows continuous 20deployment badge svg branch master codecov https codecov io gh growingio gio design branch master graph badge svg token 9ksbw04x3z https codecov io gh growingio gio design npm downloads of month https img shields io npm dm gio design components npm version https img shields io npm v gio design components fossa status https app fossa com api projects git 2bgithub com 2fgrowingio 2fgio design svg type shield https app fossa com projects git 2bgithub com 2fgrowingio 2fgio design ref badge shield license https img shields io github license growingio gio design yarn install yarn build watch yarn watch yarn start license fossa status https app fossa com api projects git 2bgithub com 2fgrowingio 2fgio design svg type large https app fossa com projects git 2bgithub com 2fgrowingio 2fgio design ref badge large alt https repobeats axiom co api embed d97950a5273fc1c63612f4e41aae8c653311ed06 svg repobeats analytics image powered by vercel public powered by vercel svg vercel vercel https vercel com utm source gio design utm campaign oss | growingio design-system react | os |
Sacchon | sacchon software application delivered by team 3 for pfizer s software and cloud engineering bootcamp theodoros antonaros constantina tsechelidou | cloud |
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awesome-vim-llm-plugins | awesome vim llm plugins below is an exhaustive list of vim and neovim plugins hosted on github which make use of large language models and have commits after january 1 2023 to optimize for maximum freshness plugins are listed in order of last commit date code writing and editing mature fully featured configurable plugins are highlighted in bold 2023 01 16 naps62 pair gpt nvim https github com naps62 pair gpt nvim 35 inline model openai 2023 02 08 jdnewman85 openai vim https github com jdnewman85 openai vim 5 inline model openai 2023 04 06 thmsmlr gpt nvim https github com thmsmlr gpt nvim 20 inline model openai 2023 04 06 aduros ai vim https github com aduros ai vim 265 inline model openai 2023 05 02 dpayne codegpt nvim https github com dpayne codegpt nvim 698 inline templates model openai 2023 06 25 madox2 vim ai https github com madox2 vim ai 354 inline chat templates model openai 2023 07 03 oelmekki make my code better vim https github com oelmekki make my code better vim 2 inline model openai 2023 07 13 tom doerr vim codex https github com tom doerr vim codex 245 inline model openai 2023 07 20 gsuuon llm nvim https github com gsuuon llm nvim 38 inline templates model openai model bard model huggingface model local 2023 08 02 codercooke vim chatgpt https github com codercooke vim chatgpt 141 inline model openai 2023 08 16 jackmort chatgpt vim https github com jackmort chatgpt vim 2500 inline workflow chat templates model openai 2023 08 18 dense analysis neural https github com dense analysis neural 313 inline model openai 2023 08 19 jayli nvim ai coding https github com jayli nvim ai coding 6 inline model openai 2023 03 18 0xstabby chatgpt vim https github com 0xstabby chatgpt vim 47 inline model chatgpt model openai conversation focused these plugins are all quite similar in functionality robitx gp nvim stands out with a rich set of configuration options and also includes commands for writing and editing code i e overlaps with the above section 2023 01 07 lambdalisue butler vim https github com lambdalisue butler vim 29 chat model openai 2023 03 26 iwasakiyuuki ai assistant nvim https github com iwasakiyuuki ai assistant nvim 3 chat model openai 2023 04 22 macrat askgpt vim https github com macrat askgpt vim 2 chat model openai 2023 07 26 yuki yano ai review vim https github com yuki yano ai review vim 16 chat model openai 2023 08 11 charlespascoe vim chatgpt https github com charlespascoe vim chatgpt 1 chat model openai 2023 08 12 micheam ai assistant console https github com micheam ai assistant console 0 chat model openai 2023 08 23 camdenclark flyboy https github com camdenclark flyboy 25 chat model openai 2023 08 28 robitx gp nvim https github com robitx gp nvim 55 inline chat templates model openai 2023 08 29 martineausimon nvim bard https github com martineausimon nvim bard 21 chat model bard tab completion these plugins are also pretty much identical in functionality and perhaps more important to compare is 1 how much subscription costs and 2 quality of the output one plugin that stands out is huggingface llm nvim which uses free inference endpoints hosted on hugging face 2023 05 10 tzachar cmp tabnine https github com tzachar cmp tabnine 263 autocomplete model custom 2023 08 22 github copilot vim https github com github copilot vim 6000 autocomplete model custom 2023 08 25 zbirenbaum copilot lua https github com zbirenbaum copilot lua 1400 autocomplete model custom 2023 08 28 codota tabnine nvim https github com codota tabnine nvim 206 autocomplete chat model custom 2023 08 30 exafunction codeium vim https github com exafunction codeium vim 2100 autocomplete model custom 2023 08 31 huggingface llm nvim https github com huggingface llm nvim 243 autocomplete model huggingface 2023 08 31 zbirenbaum copilot cmp https github com zbirenbaum copilot cmp 723 autocomplete model custom 2023 09 01 tabbyml tabby https github com tabbyml tabby 9500 autocomplete model custom other james1236 backseat nvim provides commentary in between lines of code and svermeulen text to colorscheme helps set the mood while programming 2023 04 15 james1236 backseat nvim https github com james1236 backseat nvim 143 augment model openai 2023 05 27 mthbernardes codeexplain nvim https github com mthbernardes codeexplain nvim 79 augment model local 2023 06 26 svermeulen text to colorscheme https github com svermeulen text to colorscheme 187 other model openai tag explanations functionality inline writes edits or annotates code in the current buffer a popup window or tab might be used in limited circumstances to display information chat implements an interface focused on conversation without significant support for copying to from buffers templates supports building custom commands prompts or pipelines workflow significant functionality for editing code or viewing diffs before committing changes to the current buffer augment augments the programming experience somehow but does not write or edit code other not related to programming but still uses ai for some purpose within the editor models model openai openai api model chatgpt chatgpt web interface without api model bard google palm api model huggingface hugging face inference api model local local model e g invokes llama cpp model custom any other model without an officially open api | ai |
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polymcu | introduction polymcu http labapart com products polymcu has been designed from the beginning to be as flexible as possible host os independent support linux windows macos support any toolchain gcc llvm any rtos arm rtx freertos any micro controller vendor sdk nordic semiconductor nxp freescale st enabling such flexibility provides by the same time better software quality by testing the same piece of software in various configurations it supports c application examples baremetal main c and c application examples baremetal main cpp languages the framework is based on cmake https cmake org it provides some examples to build baremetal and rtos based projects in opposition to arm mbed that provides its own library polymcu used newlib https sourceware org newlib no new interface layout has been introduced in the framework the abstraction layout for arm architecture is driven by arm cmsis v3 0 quick start for appnearme s micronfcboard here board appnearme readme md to port a new vendor sdk to polymcu here doc portvendorsdk md build install cmsis rtos conformance test here application labapart cmsis rtos conformance readme md support cmake version 2 8 toolchains gcc 4 9 2014 https launchpad net gcc arm embedded 4 9 4 9 2014 q4 major clang 3 6 0 2ubuntu1 trusty1 tags release 360 final based on llvm 3 6 0 rtos arm rtx v4 79 freertos v8 2 3 riot os 2015 09 boards appnearme micronfcboard freescale freedom kl25 nordic nrf52 preview dk nxp lp1768 mbed st stm32l476 nucleo features the application defined by application can live out of the polymcu tree if application defined an absolute path status the latest test results are available at http labapart com products polymcu test results toolchain host board linux gcc linux llvm windows appnearme micronfcboard pass pass pass freescale freedom kl25 pass pass pass nordic nrf52 preview dk pass pass pass nxp lp1768 mbed pass pass pass st stm32l476 nucleo pass pass not tested application board baremetal cmsis rtos freertos appnearme micronfcboard pass pass fail freescale freedom kl25 pass pass pass nordic nrf52 preview dk pass pass pass nxp lp1768 mbed pass pass pass st stm32l476 nucleo pass pass pass building on linux the cross compilation toolchain is either in your path or defined by the environment variable cross compile the latest cross compilation toolchain for arm cortex m can be found at https launchpad net gcc arm embedded it is recommended to build out of tree to do that create a new directory mkdir build cd build case when the application can support multiple board cmake dapplication application vendor application name dboard board vendor board name make case when the application targets only a specific board cmake dapplication application vendor application name make to build a release build cmake dapplication application vendor application name dcmake build type release make to make the build verbose cmake dapplication application vendor application name make verbose 1 to build with clang cc path to clang cmake dapplication application vendor application name make building on windows requirements install cmake https cmake org download install the latest gcc v4 9 2015q3 for arm cortex m https launchpad net gcc arm embedded 4 9 4 9 2015 q3 update download gcc arm none eabi 4 9 2015q3 20150921 win32 zip install mingw http sourceforge net projects mingw files installer mingw get setup exe download mingw32 base mingw32 gcc g build 1 download the latest sources of polymcu at https github com labapart polymcu archive master zip 2 un archive master zip 3 start a command line shell ie cmd exe 4 add cmake and mingw to your path if it is not already done for instance set path c program files x86 cmake bin path set path c mingw bin path 5 add your toolchain into the cross compile for instance set cross compile c users olivier gcc arm none eabi 4 9 2015q3 20150921 win32 bin arm none eabi 6 create the build directory into polymcu root cd polymcu root mkdir build cd build 7 optional to build with llvm set path c program files x86 llvm bin path set cc clang exe 8 build the project cmake g mingw makefiles dapplication application vendor application name dboard board vendor board name mingw32 make to make the build verbose mingw32 make verbose 1 support all cmake variables that do not start with cmake and are defined in cmake binary dir polymcu config h which is generated at build time this include file can be included in your project to access cmake configuration variables basic variables cmake variable value description firmware heap integer size in bytes of the firmware heap firmware stack integer size in bytes of the firmware stack support run from ram 0 1 define the firmware must be built to run from ram external project in binary dir 0 1 build the external project into the binary directory instead of the source directory support debug uart none itm usb 1 define which uart support to use for debugging debug uart baudrate integer debug uart baud rate default 115200 support timer 0 1 add polymcu timer api support timer systick 0 1 use systick for polymcu timer api default 1 timer task max integer number maximum of polymcu timer tasks default 5 support rtos string enable rtos support with the name of specified rtos support watchdog 0 1 add polymcu watchdog api support ram vector table 0 1 tell if the vector table lives in ram device variables cmake variable value description support device usb 0 1 add usb device support support device usb serial 0 1 add serial usb device support support device usb hid 0 1 add hid usb device support support device usb dfu 0 1 add dfu usb device support support device usb msc 0 1 add msc usb device support support ble central 0 1 add bluetooth low energy ble central support support ble peripheral 0 1 add bluetooth low energy ble peripheral support support i2c 0 1 add i2c support support spi 0 1 add spi support usb specific variables cmake variable value description device usb vendor id integer usb vendor id device usb product id integer usb product id device usb device revision integer usb device revision device usb device manufacturer string usb device manufacturer string device usb device product string usb device product string device usb device serial string usb device serial number string device usb hid input report size integer size of the usb hid input report device usb hid output report size integer size of the usb hid output report device usb hid feature report size integer size of the usb hid feature report rtos variables cmake variable value description support rtos no cmsis 0 1 disable cmsis wrapper of the rtos rtos clock integer frequency in hz of the processor rtos tick integer when os systick is not set we might need to provide a different tick rtos task count integer number of rtos task rtos task stack size integer size in bytes of the task excluding the main and private tasks rtos main stack size integer size in bytes of the main task rtos idle stack size integer size in bytes of the idle task rtos timer stack size integer size in bytes of the timer task rtos timer callback count integer number of concurrent active timer callback functions rtos task private stack count integer number of private tasks rtos task private stack size integer size in bytes of the private task rtos stack watermark 0 1 disable enable the stack watermark device specific variables cmake variable value description support nxp use xtal 0 1 use external oscillator instead of the internal one debug run an application from ram to build the firmware to run from ram cmake dapplication application vendor application name dsupport run from ram 1 make debug with gdb 1 start the debugger server pyocd gdbserver 2 start the gdb client arm none eabi gdb filepath of the elf application target remote localhost 3333 continue examples of some gdb commands gdb print pc 1 void 0x200000d8 gdb print sp 2 void 0x1fffff58 gdb print x 0x400 3 0x21004692 gdb set int 0x20000000 1 gdb set arm force mode thumb gdb display 10i 0x0 1 x 10i 0x0 0x0 vectors strh r0 r0 0 0x2 vectors 2 movs r0 0 0x4 vectors 4 lsls r1 r1 24 0x6 vectors 6 movs r0 r0 0x8 vectors 8 lsls r1 r7 24 0xa vectors 10 movs r0 r0 0xc vectors 12 adds r0 37 0x25 0xe vectors 14 movs r0 r0 0x10 vectors 16 movs r0 r0 0x12 vectors 18 movs r0 r0 gdb display 10i pc 2 x 10i pc 0x1a96 arm usart send 18 ldr r3 sp 16 0x1a98 arm usart send 20 ldrb r3 r3 0 0x1a9a arm usart send 22 mov r0 r3 0x1a9c arm usart send 24 bl 0x2da8 app uart put 0x1aa0 arm usart send 28 str r0 sp 12 0x1aa2 arm usart send 30 ldr r3 sp 12 0x1aa4 arm usart send 32 cmp r3 0 0x1aa6 arm usart send 34 bne n 0x1a96 arm usart send 18 0x1aa8 arm usart send 36 ldr r3 sp 16 0x1aaa arm usart send 38 adds r3 1 dump memory into a file gdb dump binary memory tmp gdb bin 0x0 0x1000 debug uart settings all the board uarts are set with the following settings baud rate 115200 data bits 8 stop bits 1 parity none flow control none | rtos toolchain cmake cross-compiler-toolchain gcc clang microcontroller arm iot | os |
chessboard-vision | chessboard vision chessboard computer vision project project overview i would like to create a dataset of annotated chess board images with the board setup then i would like to train a deep learning model to classify chess board positions using nothing but images this may be a good dataset for a community kaggle competition pogchamps eventually the project would produce an app that could be used in real time to track a game s progress using video workflow 0 read existing papers on the topic https web stanford edu class cs231a prev projects 2016 cs 231a final report pdf http vision fe uni lj si cvww2016 proceedings papers 21 pdf 1 gather the data youtube scraping need to hand label or extract from game labels my own images setup the board and label things to figure out 1 how the labels will look fen 2 test out object detection reference https github com paethon chess image dataset 3d image creation of chess boards todo march 22 2022 1 automatically identify x y limits of the gt board 2 crop to the irl board todo april 3 2022 | ai |
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pubsub | pubsub introduction this is a very lightweight publish subscribe framework for embedded systems written in c89 it is targeted for the zephyr rtos framework and therefore uses some zephyr libraries but it can be easily ported to other frameworks see below the library consists of a single source file src pubsub c and a single header include pubsub pubsub h it currently supports a single publisher and multiple subscribers per data topic it is designed to be thread safe and as efficient as possible with minimal overhead while offering a very simple api this makes it suitable for anything from periodic events to high rate khz sensor data exchange between threads warning pubsub is early in development it has been shown to work reliably for standard use cases involving inter thread communication of sensor data using zephyr on an stm32 device although there is no stm32 specific code other than higher resolution timestamping it may not work for certain use cases or environments if you find a bug please file an issue or send a pr installation west zephyr for west managed zephyr workspaces just add this project to your west manifest yaml manifest remotes name pubsub url base https github com coderkalyan projects name pubsub path modules lib pubsub remote pubsub non west zephyr if you are not using west or zephyr you can simply copy src pubsub c and include pubsub pubsub h into your own tree keep in mind that if you are not using zephyr you will have to port the library by providing replacements for the few zephyr specific apis used usage a complete example can be found in the examples directory the code here is shortened for brevity pubsub follows a single publisher multi subscriber architecture but first we need to define the data type we want to publish create a file in your project include topics my message h c include pubsub pubsub h pubsub types are just c structs contained in header files like this one struct my message s add your data types here int counter1 int counter2 all messages should contain 64 bit microsecond timestamps at time of writing the internal library does not make use of this timestamp but this field is reserved for future use additionally many most applications are likely to benefit from message timestamping so it s a good idea to include it int64 t timestamp the following line allows the message topic to be shared between the publisher and subscriber s extern struct pubsub topic s my message topic now that we have a data type we can start publishing to it src publisher c c include pubsub pubsub h include topics my message h this macro statically initializes the topic and can only be used in a single file per topic pubsub topic define my message topic sizeof struct my message s static void publisher thread entry point void struct my message s message memset message 0 sizeof struct my message s while 1 how you want to handle timestamps is up to you but it s good practice to populate it with something even if it isn t accurate down to the microsecond message timestamp get current time us not a real function message counter1 1 message counter2 2 publish the message on the my message topic topic on channel 0 channels allow you to publish multiple streams on the same topic for instance you might have a temperature sample topic and want to publish independent samples from 2 separate temperature sensors if you only plan to publish to a single channel use channel 0 pubsub publish my message topic 0 message sleep ms 500 not a real function now that we have a sample being published at 2hz we can subscribe to it there are 2 main ways to do this by checking for updates on a topic or using the polling api let s start with manually checking for updates if your subscriber thread only needs to check for new data relatively infrequently or you want to synchronize new data subscriptions to the subscriber thread s clock this is what you want src subscriber1 c c include pubsub pubsub h include topics my message h this macro statically defines a subscriber my message sub and subscribes it to my message topic on channel 0 pubsub subscriber define my message topic my message sub 0 static void subscriber thread entry point void struct my message s message while 1 have we gotten any new published data since last time if pubsub subscriber updated my message sub if so copy it into our own struct pubsub copy my message sub message printf received counter1 d counter2 d n message counter1 message counter2 sleep ms 1000 not a real function the other way to subscribe to data is through the polling api which piggybacks zephyr s k poll api this way is slightly less code much lower latency and does not waste resources manually polling for new data and is especially useful when your thread is consistently consuming high rate data samples from a publisher the downside is that the poll api suspends your thread until the kernel is alerted that new data has arrived so the subscriber thread cannot keep its own clock pubsub can have multiple threads subscribed to the same topic at the same time using the manual and or poll api try running this second thread at the same time src subscriber2 c c include pubsub pubsub h include topics my message h this macro statically defines a subscriber my message sub and subscribes it to my message topic on channel 0 pubsub subscriber define my message topic my message sub 0 static void subscriber thread entry point void struct my message s message int ret while 1 this function will return once new data has arrived or upon timeout 1000ms in this case ret pubsub poll my message sub k msec 1000 ret returns a positive value if new data was successfully returned 0 if the poll timed out negative if an error occured while polling if ret 0 got new data copy it into our own struct pubsub copy my message sub message printf received counter1 d counter2 d n message counter1 message counter2 else if ret 0 printf warning did not receive new data for 1000ms continuing poll n else printf error error while polling d n ret return when to use this this is by no means an exhaustive list of considerations when choosing whether or not to use pubsub however since the use case for pubsub differs from some of the other built in communication utilities in zephyr here s a brief overview use pubsub when you need high rate communication i e sensor data use pubsub when you want a single publisher multi subscriber api use pubsub when your data format structure is static not different between samples don t use pubsub when you need to store or batch multiple samples in a queue or stack use a fifo if you need a single subscriber api with potentially varying data structures use a mailbox when you need bidirectional data transfer porting to port pubsub to another framework you will have to provide the following interfaces a c heap implementation malloc free a linked list implementation semaphores mutexes for multithreading optional a kernel polling api for poll based usage a high resolution timestamping implementation is very useful for usage microsecond resolution is preferred but millisecond resolution is sufficient | hacktoberfest | os |
E-commerce-microservices | ecommerce microservices alt text screenshots flower store png this is a simple cloud application developed alongside the udacity cloud engineering nanodegree users type there are two types of users admin client use cases it allows admins to login list the products create delete a product list the client orders and accept or reject an order client to check the products and order a product global architecture alt text screenshots architecture jpg create docker images and run it locally build the images docker compose f docker compose build yaml build parallel starting the app as a container on a local system docker compose up to share those images through the docker hub docker compose f docker compose build yaml push ps i could not share the images on docker hub because of my slow internet i only shared the yahiakr ecommerce reverseproxy https hub docker com r yahiakr ecommerce reverseproxy image because it s lightweight deploy the application to to a kubernetes cluster 1 first we create the infrastructure kubernetes cluster using the aws management console this article https medium com faun create your first application on aws eks kubernetes cluster 874ee9681293 guides you to set up your kubernetes cluster in aws eks 2 setup the credentials secrets kubectl apply f env configmap yaml kubectl apply f aws secret yaml kubectl apply f env secret yaml 3 launch the deployments and the services in your kubernetes cluster as follows kubectl apply f users microservice deployment yaml kubectl apply f products microservice deployment yaml kubectl apply f orders microservice deployment yaml kubectl apply f admin frontend deployment yaml kubectl apply f client frontend deployment yaml kubectl apply f reverseproxy deployment yaml launch the services kubectl apply f users microservice service yaml kubectl apply f products microservice service yaml kubectl apply f orders microservice service yaml kubectl apply f admin frontend service yaml kubectl apply f client frontend service yaml kubectl apply f reverseproxy service yaml other informations you can acces the dashboard from http localhost 8100 http localhost 8100 the client app from http localhost 8000 http localhost 8000 you can test the services using postman collections each service has it s own postman collection in it s root folder postman collections contain tests to login to the dashboard use the following credentials email admin gmail com password admin ps i didn t add the sign up because in a real world scenario only the admin can add other admins but to test it you can create an account using postman request in users postman collection a client can order a product by adding his email no address or payment method just for simplicity because it s not the objectif of the project | nodejs aws microservices | cloud |
MalwareTrainingSets | malwaretrainingsets please check it out https marcoramilli com 2016 12 16 malware training sets a machine learning dataset for everyone for an updated followup please check it out https marcoramilli com 2019 05 14 malware training sets followup cite the dataset if you find those results useful please cite them misc mr author marco ramilli title malware training sets a machine learning dataset for everyone year 2016 url https marcoramilli com 2016 12 16 malware training sets a machine learning dataset for everyone note online december 2016 update many people asked me about the scripts i used to generate mist modified json so here there are take a look to scripts section you might use mist json py as a reporting module from cuckoosandbox and the script frommongotoarff py to generate arff files suitables for weka if you are going to create new datasets by running your local cuckoosandbox using mist json py module and you wanto to share them please feel free to make pool requests if you want to know more about the working flow please check this update https marcoramilli com 2019 05 14 malware training sets followup | malware malware-analysis machine-learning training-set | ai |
ml-on-gcp | machine learning on google cloud platform guides to bringing your code from various machine learning frameworks to google cloud platform the goal is to present recipes and practices that will help you spend less time wrangling with the various interfaces and more time exploring your datasets building your models and in general solving the problems you really care about blog posts 1 genomic ancestry inference with deep learning https cloud google com blog big data 2017 09 genomic ancestry inference with deep learning ancestry inference on google cloud platform using the 1000 genomes dataset https cloud google com genomics data 1000 genomes 2 running tensorflow inference workloads at scale with tensorrt 5 and nvidia t4 gpus https cloud google com blog products ai machine learning running tensorflow inference workloads at scale with tensorrt 5 and nvidia t4 gpus creating a demo of ml inference using tesla t4 tensorflow tensorrt load balancing and auto scale 3 nvidia s rapids joins our set of deep learning vm images for faster data science https cloud google com blog products ai machine learning nvidias rapids joins our set of deep learning vm images for faster data science google cloud s set of deep learning virtual machine vm images which enable the one click setup machine learning focused development environments but some data scientists still use a combination of pandas dask scikit learn and spark on traditional cpu based instances if you d like to speed up your end to end pipeline through scale google cloud s deep learning vms now include an experimental image with rapids nvidia s open source and python based gpu accelerated data processing and machine learning libraries that are a key part of nvidia s larger collection of cuda x ai accelerated software cuda x ai is the collection of nvidia s gpu acceleration libraries to accelerate deep learning machine learning and data analysis 4 inferring machine learning models from google cloud functions https cloud google com blog products ai machine learning tbd introduction to inferring ai platform models from google cloud function endpoints 4 nvidia achieves breakthroughs in language understanding to enable real time conversational ai https nvidianews nvidia com news nvidia achieves breakthroughs in language understandingto enable real time conversational ai ncid so elev 49597 cid organicsocial en us elevate deep learning ai for developers dl13 bert notebook in ai hub and ai platform notebooks tensorflow 1 estimators tutorials tensorflow tf estimators ipynb a guide to the estimator interface scikit learn 1 scikit learn on gce tutorials sklearn titanic train a simple model with scikit learn on a google compute engine 2 model serve tutorials sklearn gae serve serve model with google app engine and cloud endpoints 3 hyperparameter search tutorials sklearn hpsearch hyperparameter search on a google kubernetes engine cluster from a jupyter notebook google compute engine 1 compute engine survival training gce survival training readme md introduces a framework for running resilient training jobs on google compute engine 2 compute engine burst training gce burst training readme md a guide to using powerful vms to quickly and cheaply perform computationally intensive training jobs the example training job in this guide uses xgboost https github com dmlc xgboost as well as scikit learn http scikit learn org stable google cloud functions 1 google cloud functions ai platform example gcf gcf ai platform example readme md example endpoints to infer ai platform models example zoo collections of examples adapted to be runnable on ai platform https cloud google com ai platform 1 tensorflow probability examples example zoo tensorflow probability 1 tensorflow models examples example zoo tensorflow models google machine learning repositories if you re looking for our guides on how to do machine learning on google cloud platform gcp using other services please checkout our other repositories ai platform samples https github com googlecloudplatform ai platform samples which has guides on how to bring your code from various ml frameworks to google cloud ai platform using different products such as ai platform training prediction notebooks and ai hub keras idiomatic programmer https github com googlecloudplatform keras idiomatic programmer this repository contains content produced by google cloud ai developer relations for machine learning and artificial intelligence the content covers a wide spectrum from educational training and research covering from novices junior intermediate to advanced professional services https github com googlecloudplatform professional services common solutions and tools developed by google cloud s professional services team | samples | ai |
ctec101_Information_Technology_Principles | ctec101 information technology principles | server |
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ui | dhis2 ui dhis2 ui on npm https badge fury io js 40dhis2 2fui svg https www npmjs com package dhis2 ui semantic release https img shields io badge 20 20 f0 9f 93 a6 f0 9f 9a 80 semantic release e10079 svg https github com semantic release semantic release conventional commits https img shields io badge conventional 20commits 1 0 0 yellow svg https conventionalcommits org dhis2 ui is a suite of frontend components for building dhis 2 applications to install dhis2 ui run bash yarn install dhis2 ui all components can be imported directly from dhis2 ui like so js import button from dhis2 ui we recommend that you use dhis2 ui as the entrypoint for all imports of our frontend components as in the example above that way your imports won t break if any of the underlying packages change documentation dhis2 ui is based on the specifications in our design system https github com dhis2 design system see the documentation there for more information docs ui dhis2 nu https ui dhis2 nu live demo ui dhis2 nu demo https ui dhis2 nu demo api reference ui dhis2 nu api https ui dhis2 nu api bundled packages package link status dhis2 ui collections ui collections ui active dhis2 ui constants constants utilities constants active dhis2 ui forms collections forms collections forms active dhis2 ui icons icons utilities icons active dhis2 ui constants this package provides access to shared constants such as colors spacers and elevation values they are used across our frontend components and can be used directly in applications as well our constants can be imported like so js import colors from dhis2 ui see our api docs https ui dhis2 nu api for a full list of the available constants dhis2 ui icons this package provides a collection of icons as react components for tree shaking purposes the icon name variant and size are all expressed in the component name our icons can be imported like so js import iconapps16 from dhis2 ui for a list of all the available icons see the ui icons package https github com dhis2 ui tree master packages icons src note that during their transformation to react components the svg filenames are pascalcased and prefixed with icon so apps 16 svg becomes iconapps16 and can then be imported as in the example above the default fill of our icons is inherited from color with currentcolor to set a custom icon color you can use the color prop like so jsx iconapps16 color de683d dhis2 ui forms this package provides several components that allow for easy integration of our form components with react final form https github com final form react final form besides form components we also export several validator functions for common usecases components from this library can be imported like so js import textareafieldff from dhis2 ui the ff suffix ensures that these components don t clash with our regular field components from the widgets package and is an abbreviation of final form see our api docs https ui dhis2 nu api or the live docs https ui dhis2 nu demo for a full list of the available components and validators development sh git clone git github com dhis2 ui git cd ui yarn install yarn d2 style install yarn setup yarn start in case manager complains about the main manager bundle js e g cannot import dhis2 ui constants then use yarn start no manager cache reporting an issue or opening a pr see contributing md contributing md | web-lib synced-settings hacktoberfest | os |
GeneralRepositoriesCoursera | generalrepositoriescoursera reposit rio coursera para programa de cursos integrados data engineering on google cloud platform em portugu s brasileiro | cloud |
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aws-iot-twinmaker-samples | 2022 11 17 for a sample digital twin application highlighting twinmaker knowledge graph https aws amazon com about aws whats new 2022 11 twinmaker knowledge graph generally available aws iot twinmaker check out our guided smartbuilding workshop https catalog us east 1 prod workshops aws workshops 93076d98 bdf1 48b8 bfe8 f4039ca1bf25 en us note if you are just looking for sample iam policies to use when creating an aws iot twinmaker workspace please see these sample permission docs sample workspace role permission policy json and trust relationship docs sample workspace role trust policy json policies if you would like to create this role using aws cloudformation https console aws amazon com cloudformation home stacks create template please use this template docs sample workspace role yml the role permission policy will only grant aws iot twinmaker access to manage workspace resources in your s3 buckets we recommend you scope down the bucket permissions to your specific s3 bucket once it is created you will also need to update the role to grant further permissions for your use case such as invoking aws iot twinmaker custom aws lambda connectors you ve implemented or accessing video stream metadata in aws iot sitewise and amazon kinesis video streams for an end to end setup experience including auto generation of these roles with all necessary permissions for the sample use case we recommend following the getting started guide below aws iot twinmaker getting started summary this project walks you through the process of building a digital twin application using aws iot twinmaker the project contains many samples including a simulated cookie factory that you can use to explore many of the features of iot twinmaker after going through this readme you will have the following dashboard running in grafana which you can use to interact with the sample cookiefactory digital twin grafana import cookiefactory docs images grafana import result cookiefactory png if you run into any issues please see the troubleshooting section of this page prerequisites note these instructions have primarily been tested for mac linux wsl environments for a standardized development environment consider following our cloud9 setup guide cloud9 setup md instead 1 this sample depends on aws services that might not yet be available in all regions please run this sample in one of the following regions us east n virginia us east 1 us west oregon us west 2 europe ireland eu west 1 2 an aws account for iot twinmaker aws cli https docs aws amazon com cli latest userguide install cliv2 html we recommend that you configure https docs aws amazon com cli latest userguide cli chap configure html your default credentials to match the account in which you want to set up this getting started example use the following command to verify that you are using the correct account this should be pre configured in cloud9 bash aws sts get caller identity ensure your aws cli version is at least 1 22 94 or 2 5 5 for aws cli v2 bash aws version when you are set up test your access with the following command you should not receive errors aws iottwinmaker list workspaces region us east 1 3 python3 https www python org downloads verify your python3 path and version 3 7 this should be pre installed in cloud9 python3 version optional pyenv https github com pyenv pyenv and pyenv virtualenv https github com pyenv pyenv virtualenv use pyenv and pyenv virtualenv to ensure that you have correct python dependencies they are optional as long as you have a system wide python3 installation but highly recommended for avoiding conflicts between multiple python projects 4 node js npm https nodejs org en with node v14 18 1 and npm version 8 10 0 this should be pre installed in cloud9 use the following commands to verify node version npm version 5 aws cdk toolkit https docs aws amazon com cdk latest guide getting started html getting started install with version at least 2 27 0 the cdk should be pre installed in cloud9 but you may need to bootstrap your account use the following command to verify cdk version you will also need to bootstrap your account for cdk so that custom assets such as sample lambda functions can be easily deployed use the following command cdk bootstrap aws your 12 digit aws account id region example cdk bootstrap aws 123456789012 us east 1 6 docker https docs docker com get docker version 20 this should be pre installed in cloud9 authenticate docker for public ecr registries docker version use the following command to build lambda layers for cdk bash aws ecr public get login password region us east 1 docker login username aws password stdin public ecr aws deploying the sample cookie factory workspace 1 set up environment variables set the following environment variables to make it easier to execute the remaining steps bash change into the same directory as this readme cd directory of this readme bash set your aws account id you can use aws sts get caller identity to see the account id you re currently using export cdk default account replace with your aws account id bash set some options for our install if you want to use another workspace id then change cookiefactory to your preference export getting started dir pwd export aws default region us east 1 export cdk default region aws default region export timestream telemetry stack name cookiefactorytelemetry export workspace id cookiefactory 2 install python libraries we use python to help deploy our cookie factory sample data use the following command to install the required python libraries bash pip3 install r getting started dir src workspaces cookiefactory requirements txt 3 create an iot twinmaker workspace a create an iot twinmaker execution role different digital twin applications use different resources run the following command to create an execution role for our workspace that has the necessary permissions for this sample application note that you will use the role name when creating a workspace in the next step bash python3 getting started dir src workspaces cookiefactory setup cloud resources create iottwinmaker workspace role py region aws default region b create the workspace in the aws console now go to the console and create a workspace with the same name that you used for workspace id in step 1 you can have the console automatically create s3 buckets for you when asked to provide a role for the workspace use the role name generated by the preceding script the name should contain the string iottwinmakerworkspacerole after entering the workspace settings you will be asked to specify the grafana environment you will be using to interact with your workspace we recommend you use amazon managed grafana to host your infrastructure but you can follow grafana instructions docs grafana local docker setup md to make your decision and build the environment when asked for your dashboard role you can follow the instructions in the console to manually create an iam policy and role to be used in grafana you can automatically create the role using the script in this package in the next step after creating the workspace finally click create on the review page to create your workspace console link for us east 1 https us east 1 console aws amazon com iottwinmaker home region us east 1 c create a grafana dashboard iam role if you did not complete the dashboard setting steps run the following script to create a role for accessing the workspace on a grafana dashboard this uses scoped down permissions for readonly access to iot twinmaker and other aws services in grafana note the arn of the role you create you will use it when configuring a data source in grafana bash python3 getting started dir src modules grafana create grafana dashboard role py workspace id workspace id region aws default region account id cdk default account if you are using amazon managed grafana add the field bash auth provider amazon managed grafana workspace iam role arn make sure that your current aws credentials are the same as the ones you use in grafana if not go to the iam console after running this script and update the trust permissions for the authentication provider you will be using read more about your authentication provider in the documentation https docs aws amazon com iot twinmaker latest guide dashboard iam role html grafana iam role we automatically add permission for iot twinmaker and kinesis video streams to enable the basic functionality of the grafana datasource scene viewer panel and video player panel if you would like to enable more features of the video player time scrubber bar video upload request from cache then you need to manually update your iam policy by following our video player policy documentation https docs aws amazon com iot twinmaker latest guide tm video policy html 4 deploy an instance of the timestream telemetry module timestream telemetry is a sample telemetry store for iot data it uses a single aws timestream database and table and a lambda function for reading and writing later steps will fill this table with sample data for the cookie factory the following commands create the database and table and deploy the lambda function found under src lib timestream telemetry bash cd getting started dir src modules timestream telemetry cdk use the following command to install dependencies for the module npm install deploy the module enter y when prompted to accept iam changes cdk deploy 5 use the following commands to import the cookie factory content bash cd getting started dir src workspaces cookiefactory import cookie factory data into your workspace python3 m setup content telemetry stack name timestream telemetry stack name workspace id workspace id region name aws default region import all if you want to reimport the sample content you need to add flags to delete the old content such as delete all or individual flags such as delete telemetry and delete entities if you want to import only parts of the sample content you can use individual import flags instead of import all such as import telemetry and import entities note on initial import the script will save the starting timestamp used for generating sample telemetry and video this is stored in the twinmaker workspace in the samples content start time tag on subsequent re runs of the scripts this starting timestamp will be re used for consistent data generation if you would like to recreate the data using the current time instead please delete the tag from the workspace 6 optional verify connectivity for entities scenes and unified data query udq test data by using udq after importing all content you can go to the iot twinmaker console to view the entities and scenes that you created https us east 1 console aws amazon com iottwinmaker home region us east 1 aws iot twinmaker provides features to connect to and query your data sources via its component model and unified data query interface in this getting started we imported some data into timestream and set up the component and support udq lambda function that enables us to query it use the following command to test whether we re able to query for alarm data by using the get property value history api aws iottwinmaker get property value history region aws default region cli input json componentname alarmcomponent endtime 2023 06 01t00 00 00z entityid mixer 2 06ac63c4 d68d 4723 891a 8e758f8456ef orderbytime ascending selectedproperties alarm status starttime 2022 06 01t00 00 00z workspaceid workspace id see additional udq sample requests additional udq sample requests for other supported request examples 7 set up grafana for the cookie factory aws iot twinmaker provides a grafana plugin that you can use to build dashboards using iot twinmaker scenes and modeled data sources grafana is deployable as a docker container we recommend that new users follow these instructions to set up grafana as a local container instructions docs grafana local docker setup md if the link doesn t work in cloud9 open docs grafana local docker setup md for advanced users aiming to set up a production grafana installation in their account we recommend checking out https github com aws samples aws cdk grafana 8 import grafana dashboards for the cookie factory when you have the grafana page open you can click through the following to import the sample dashboard json file in getting started dir src workspaces cookiefactory sample dashboards if you are running from cloud9 you can right click and download the file locally then import it from your local machine mixer alarms dashboard json grafana import cookiefactory docs images grafana import dashboard png for the cookiefactory sample running with local grafana you can navigate to http localhost 3000 d y1fgfj57z aws iot twinmaker mixer alarm dashboard orgid 1 to see the dashboard deploying additional add on content sitewise connector in this section we ll add sitewise assets and telemetry and then update the cookiefactory digital twin entities to link to this data source 1 add sitewise assets and telemetry python3 getting started dir src modules sitewise deploy utils sitewisetelemetry py import csv file getting started dir src workspaces cookiefactory sample data telemetry telemetry csv entity include pattern watertank asset model name prefix workspace id 2 update entities to attach sitewise connector python3 getting started dir src modules sitewise lib patch sitewise content py workspace id workspace id region aws default region 3 test sitewise data connectivity with udq to query watertank volume metrics aws iottwinmaker get property value history region aws default region cli input json componentname watertankvolume endtime 2023 06 01t00 00 00z entityid watertank ab5e8bc0 5c8f 44d8 b0a9 bef9c8d2cfab orderbytime ascending selectedproperties tankvolume1 starttime 2022 06 01t00 00 00z workspaceid workspace id s3 document connector in this section we ll add an s3 connector to allow iot twinmaker entities to link to data stored in s3 go to the s3 modules directory and check the readme src modules s3 readme md cd getting started dir src modules s3 aws iot twinmaker insights and simulation note this add on will create running amazon kinesis data analytics kda compute resources that may incur aws charges we recommend stopping or deleting the kda notebook resources with the steps in add on teardown aws iot twinmaker insights and simulation add on teardown aws iot twinmaker insights and simulation once you are finished using them in this section we ll use the aws iot twinmaker flink library to connect our mixers telemetry data to two services to enrich our entity data for deeper insights a maplesoft simulation to calculate mixer power consumption based on rpm a pre trained machine learning model for rpm anomaly detection both services will be exposed as sagemaker endpoints that this add on will setup in your account go to the insights modules directory and check the readme src modules insights readme md cd getting started dir src modules insights additional udq sample requests this section contains additional sample requests supported by get property value history in the cookiefactory workspace 1 single entity multi property request mixer data aws iottwinmaker get property value history region aws default region cli input json componentname mixercomponent endtime 2023 06 01t00 00 00z entityid mixer 2 06ac63c4 d68d 4723 891a 8e758f8456ef orderbytime ascending selectedproperties temperature rpm starttime 2022 06 01t00 00 00z workspaceid workspace id 2 multi entity single property request alarm data aws iottwinmaker get property value history region aws default region cli input json componenttypeid com example cookiefactory alarm endtime 2023 06 01t00 00 00z orderbytime ascending selectedproperties alarm status starttime 2022 06 01t00 00 00z workspaceid workspace id 3 multi entity multi property request mixer data aws iottwinmaker get property value history region aws default region cli input json componenttypeid com example cookiefactory mixer endtime 2023 06 01t00 00 00z orderbytime ascending selectedproperties temperature rpm starttime 2022 06 01t00 00 00z workspaceid workspace id teardown note that these are destructive actions and will remove all content you have created modified from this sample you should have the following environment variables set from the previous setup instructions bash getting started dir see above workspace id see above timestream telemetry stack name see above aws default region us east 1 add on teardown sitewise connector run the following if you installed the add on sitewise content and would like to remove it python3 getting started dir src modules sitewise deploy utils sitewisetelemetry py cleanup asset model name prefix workspace id add on teardown s3 document connector run the following if you installed the add on sitewise content and would like to remove it aws cloudformation delete stack stack name iottwinmakercookiefactorys3 region aws default region aws cloudformation wait stack delete complete stack name iottwinmakercookiefactorys3 region aws default region add on teardown aws iot twinmaker insights and simulation run the following if you installed the add on aws iot twinmaker insights and simulation content and would like to remove it these stacks may take several minutes to delete delete installed assets python3 insight dir install insights module py workspace id workspace id region name aws default region kda stack name kda stack name sagemaker stack name sagemaker stack name delete all delete cloudformation stacks aws cloudformation delete stack stack name kda stack name region aws default region aws cloudformation wait stack delete complete stack name kda stack name region aws default region aws cloudformation delete stack stack name sagemaker stack name region aws default region aws cloudformation wait stack delete complete stack name sagemaker stack name region aws default region delete base content change directory cd getting started dir src workspaces cookiefactory delete grafana dashboard role if exists python3 getting started dir src modules grafana cleanup grafana dashboard role py workspace id workspace id region aws default region delete aws iot twinmaker workspace contents this script is safe to terminate and restart if entities seem stuck in deletion python3 m setup content telemetry stack name timestream telemetry stack name workspace id workspace id region name aws default region delete all delete workspace role and bucket delete the telemetry cfn stack wait aws cloudformation delete stack stack name timestream telemetry stack name region aws default region aws cloudformation wait stack delete complete stack name timestream telemetry stack name region aws default region optional delete local grafana configuration rm rf local grafana data troubleshooting for any issue not addressed here please open an issue or contact aws support importerror libgl so 1 cannot open shared object file no such file or directory ensure you have mesa libgl installed e g sudo yum install mesa libgl security see contributing contributing md security issue notifications for more information license this project is licensed under the apache 2 0 license | server |
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jobs-api | jobs api jobs api is an express js app which is an outcome of nodejs backend development learning | server |
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datasets-server | datasets server integrate into your apps over 10 000 datasets via simple http requests with pre processed responses and scalability built in documentation https huggingface co docs datasets server ask for a new feature the datasets server pre processes the hugging face hub datasets https huggingface co datasets to make them ready to use in your apps using the api list of the splits first rows we plan to add more features https github com huggingface datasets server issues q is 3aissue is 3aopen label 3a 22feature request 22 to the server please comment there and upvote your favorite requests if you think about a new feature please open a new issue https github com huggingface datasets server issues new contribute you can help by giving ideas answering questions reporting bugs proposing enhancements improving the documentation and fixing bugs see contributing md contributing md for more details to install the server and start contributing to the code see developer guide md developer guide md community you can star and watch this github repository https github com huggingface datasets server to follow the updates you can ask for help or answer questions on the forum https discuss huggingface co c datasets 10 and discord https discord com channels 879548962464493619 1019883044724822016 you can also report bugs and propose enhancements on the code or the documentation in the github issues https github com huggingface datasets server issues | datasets machine-learning api-rest data huggingface nlp | ai |
moko-network | moko network img logo png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko network https repo1 maven org maven2 dev icerock moko network kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name network mobile kotlin network components this is a kotlin multiplatform library that provide network components for ios android library is addition to ktor client https github com ktorio ktor with gradle plugin to generate entities and api classes from openapi swagger specification file entities serialization done by kotlinx serialization https github com kotlin kotlinx serialization table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features openapi client code generation just configure plugin then use generated entities and api classes tokenfeature feature to ktor client with auth token header support exceptionfeature feature to ktor client that parse errors from server to domain exceptions refreshtokenfeature feature to ktor client that handle unauthorized response from server try to update token and repeat failed request in case when token update was successful requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy buildscript repositories gradlepluginportal dependencies classpath dev icerock moko network generator 0 21 0 allprojects repositories mavencentral project build gradle groovy apply plugin dev icerock mobile multiplatform network generator dependencies commonmainapi dev icerock moko network 0 21 0 commonmainapi dev icerock moko network engine 0 21 0 configured httpclientengine commonmainapi dev icerock moko network bignum 0 21 0 kbignum serializer commonmainapi dev icerock moko network errors 0 21 0 moko errors integration usage 1 e g put an openapi specification file to src swagger json path of the project gradle module 2 setup the project build gradle groovy mokonetwork spec pets inputspec file src swagger json spec news inputspec file src newsapi yaml packagename news isinternal false isopen true configuretask here can be configuration of https github com openapitools openapi generator generatetask enumfallbacknull false 3 then run openapigenerate gradle task and after completion you will get all generated classes in build generated moko network directory 4 to import all generated classes of model put kotlin import dev icerock moko network generated models kotlin import dev icerock moko network generated apis 5 then you can use generated api s in your application in the common sourceset kotlin import dev icerock moko network generated apis class testviewmodel viewmodel private val petapi petapi basepath https petstore swagger io v2 base api url httpclient ktorhttpclient reference to ktor http client object json kotlinxjsonparser reference to kotlinx serialization json parser object fun apirequest viewmodelscope launch try val pet petapi findpetsbystatus listof available catch error exception for the moko network specification generator you can enable safe enum properties generation mode to turn on the mode in build gradle to a spec block add flag enumfallbacknull true the enabled mode will generate special wrapper safeable for all properties with the enum type that which in a situation for an unexpected enum value will return null deprecated usage old way with openapi generator plugin available by groovy apply plugin dev icerock mobile multiplatform network generator deprecated openapigenerate inputspec set file src swagger json path generatorname set kotlin ktor client but this way limited to one spec in one time moko network errors there is module moko network errors for moko errors https github com icerockdev moko errors library that contains built in mappers for mapping moko network exception classes into stringdesc objects there are built in resources for text in english and russian to use the moko network errors module just call the registerallnetworkmappers extension of exceptionmappersstorage object kotlin fun initexceptionmappersstorage exceptionmappersstorage registerallnetworkmappers and then you can use exceptionhandler to automatically handle exceptions for network requests kotlin viewmodelscope launch exceptionhandler handle val pet petapi findpetsbystatus listof available execute for more information about exception handling see moko errors https github com icerockdev moko errors samples more examples can be found in the sample directory sample set up locally in network directory network contains network library in network errors directory network errors contains network errors module in plugins directory plugins contains gradle plugin with openapi implementation generator in sample directory sample contains samples on android ios mpp library connected to apps contributing all development both new features and bug fixes is performed in develop branch this way master sources always contain sources of the most recently released version please send prs with bug fixes to develop branch fixes to documentation in markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master during release more detailed guide for contributers see in contributing guide contributing md license copyright 2019 icerock mag inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | android ios kotlin-native kotlin-multiplatform moko ktor-client openapi gradle-plugin serialization coroutines kotlin-multiplatform-mobile | front_end |
traingenerator | h1 align center traingenerator h1 p align center strong nbsp a web app to generate template code for machine learning strong p p align center a href https twitter com jrieke img src https img shields io twitter follow jrieke color blue label follow 20 40jrieke logo twitter style plastic a a href https traingenerator streamlitapp com img src https static streamlit io badges streamlit badge black white svg a a href https www buymeacoffee com jrieke img src https img shields io badge buy 20me 20a coffee orange svg logo buy me a coffee logocolor orange a a href license img src https img shields io github license jrieke traingenerator svg a a href https github com psf black img src https img shields io badge code 20style black 000000 svg alt code style black a p br p align center img src docs assets demo gif width 600 p br h3 align center traingenerator is now live br br try it out br a href https traingenerator streamlitapp com https traingenerator streamlitapp com a h3 p align center strong try it out br a href https traingenerator streamlitapp com https traingenerator streamlitapp com a strong p br generate custom template code for pytorch sklearn using a simple web ui built with streamlit https www streamlit io traingenerator offers multiple options for preprocessing model setup training and visualization using tensorboard or comet ml it exports to py jupyter notebook or google colab https colab research google com the perfect tool to jumpstart your next machine learning project br for updates follow me on twitter https twitter com jrieke br br adding new templates you can add your own template in 4 easy steps see below without changing any code in the app itself your new template will be automatically discovered by traingenerator and shown in the sidebar that s it want to share your magic prs are welcome please have a look at contributing md contributing md some ideas for new templates keras tensorflow pytorch lightning object detection segmentation text classification img align right src docs assets dropdowns png width 160 1 create a folder under templates the folder name should be the task that your template solves e g image classification optionally you can add a framework name e g image classification pytorch both names are automatically shown in the first two dropdowns in the sidebar see image tip copy the example template templates example to get started more quickly 1 add a file sidebar py to the folder see example templates example sidebar py it needs to contain a method show which displays all template specific streamlit components in the sidebar i e everything below task and returns a dictionary of user inputs 3 add a file code template py jinja to the folder see example templates example code template py jinja this jinja2 template https jinja palletsprojects com en 2 11 x templates is used to generate the code you can write normal python code in it and modify it through jinja based on the user inputs in the sidebar e g insert a parameter value from the sidebar or show different code parts based on the user s selection 4 optional add a file test inputs yml to the folder see example templates example test inputs yml this simple yaml file should define a few possible user inputs that can be used for testing if you run pytest see below it will automatically pick up this file render the code template with its values and check that the generated code runs without errors this file is optional but it s required if you want to contribute your template to this repo installation note you only need to install traingenerator if you want to contribute or run it locally if you just want to use it go here https traingenerator streamlitapp com bash git clone https github com jrieke traingenerator git cd traingenerator pip install r requirements txt optional for the open in colab button to work you need to set up a github repo where the notebook files can be stored colab can only open public files if they are on github after setting up the repo create a file env with content bash github token your github access token repo name user notebooks repo if you don t set this up the app will still work but the open in colab button will only show an error message running locally bash streamlit run app main py make sure to run always from the traingenerator dir not from the app dir otherwise the app will not be able to find the templates deploying to heroku first install heroku and login https devcenter heroku com articles getting started with python set up to create a new deployment run inside traingenerator heroku create git push heroku main heroku open to update the deployed app commit your changes and run git push heroku main optional if you set up a github repo to enable the open in colab button see above you also need to run heroku config set github token your github access token heroku config set repo name user notebooks repo testing first install pytest and required plugins via bash pip install r requirements dev txt to run all tests bash pytest tests note that this only tests the code templates i e it renders them with different input values and makes sure that the code executes without error the streamlit app itself is not tested at the moment you can also test an individual template by passing the name of the template dir to template e g bash pytest tests template image classification scikit learn the mage image used in traingenerator is from twitter s twemoji library https github com twitter twemoji and released under creative commons attribution 4 0 international public license | machine-learning deep-learning template website webapp streamlit pytorch scikit-learn sklearn tensorflow pytorch-ignite machine-learning-tutorials pytorch-lightning | ai |
filex | azure rtos filex this is a high performance file allocation table fat compatible file system that s fully integrated with azure rtos threadx and available for all supported processors like azure rtos threadx azure rtos filex is designed to have a small footprint and high performance making it ideal for today s deeply embedded applications that require file management operations filex supports most physical media including ram azure rtos usbx sd card and nand nor flash memories via azure rtos levelx here are the key features and modules of filex filex key features docs filex features png getting started azure rtos filex as part of azure rtos has been integrated to the semiconductor s sdks and development environment you can develop using the tools of choice from stmicroelectronics https www st com content st com en campaigns x cube azrtos azure rtos stm32 html nxp https www nxp com design software embedded software azure rtos for nxp microcontrollers azure rtos renesas https github com renesas azure rtos and microchip https mu microchip com get started simplifying your iot design with azure rtos we also provide samples https github com azure rtos samples using hero development boards from semiconductors you can build and test with see overview of azure rtos filex https learn microsoft com azure rtos filex overview filex for the high level overview and all documentation and apis can be found in azure rtos filex documentation https learn microsoft com azure rtos filex repository structure and usage directory layout cmake cmakelist files for building the project common core filex files ports architecture and compiler specific files samples sample codes license txt license terms license hardware txt licensed hardware from semiconductors contributing md contribution guidance security md microsoft repo security guidance branches releases the master branch has the most recent code with all new features and bug fixes it does not represent the latest general availability ga release of the library each official release preview or ga will be tagged to mark the commit and push it into the github releases tab e g v6 2 rel when you see xx xx xxxx 6 x or x x in function header this means the file is not officially released yet they will be updated in the next release see example below function release tx initialize low level cortex m23 gnu 6 x author scott larson microsoft corporation description this function is responsible for any low level processor initialization including setting up interrupt vectors setting up a periodic timer interrupt source saving the system stack pointer for use in isr processing later and finding the first available ram memory address for tx application define input none output none calls none called by tx initialize kernel enter threadx entry function release history date name description 09 30 2020 scott larson initial version 6 1 xx xx xxxx scott larson include tx user h resulting in version 6 x exfat licensing filex supports the microsoft exfat file system format using the fx enable exfat define see fx user sample h common inc fx user sample h for more information about configuration of filex the azure rtos filex implementation of exfat technology is licensed when used in listed components https github com azure rtos filex blob master licensed hardware txt obtained through a licensed partner other implementations of exfat technology in your products may require a separate license from microsoft please see the following link for further details on exfat licensing from microsoft here https www microsoft com en us legal intellectualproperty tech licensing programs activetab pivot1 primaryr5 component dependencies the main components of azure rtos are each provided in their own repository but there are dependencies between them as shown in the following graph this is important to understand when setting up your builds dependency graph docs deps png you will have to take the dependency graph above into account when building anything other than threadx itself building and using the library instruction for building the filex as static library using arm gnu toolchain and cmake if you are using toolchain and ide from semiconductor you might follow its own instructions to use azure rtos components as explained in the getting started getting started section 1 install the following tools cmake https cmake org download version 3 0 or later arm gnu toolchain for arm none eabi https developer arm com downloads arm gnu toolchain downloads ninja https ninja build org 1 build the threadx library https github com azure rtos threadx building and using the library as the dependency 1 cloning the repo bash git clone https github com azure rtos filex git 1 define the features and addons you need in fx user h and build together with the component source code you can refer to fx user sample h https github com azure rtos filex blob master common inc fx user sample h as an example 1 building as a static library each component of azure rtos comes with a composable cmake based build system that supports many different mcus and host systems integrating any of these components into your device app code is as simple as adding a git submodule and then including it in your build using the cmake add subdirectory while the typical usage pattern is to include filex into your device code source tree to be built linked with your code you can compile this project as a standalone static library to confirm your build is set up correctly an example of building the library for cortex m4 bash cmake bbuild gninja dcmake toolchain file cmake cortex m4 cmake cmake build build professional support professional support plans https azure microsoft com support options are available from microsoft for community support and others see the resources resources section below licensing license terms for using azure rtos are defined in the license txt file of this repo please refer to this file for all definitive licensing information no additional license fees are required for deploying azure rtos on hardware defined in the licensed hardware txt licensed hardware txt file if you are using hardware not listed in the file or having licensing questions in general please contact microsoft directly at https aka ms azrtos license resources the following are references to additional azure rtos resources product introduction and white papers https azure com rtos general technical questions https aka ms qna azure rtos product issues and bugs or feature requests https github com azure rtos filex issues licensing and sales questions https aka ms azrtos license product roadmap and support policy https aka ms azrtos lts blogs and videos http msiotblog com and https aka ms iotshow azure rtos tracex installer https aka ms azrtos tracex installer you can also check previous questions https stackoverflow com questions tagged azure rtos filex or ask new ones on stackoverflow using the azure rtos and filex tags security azure rtos provides oems with components to secure communication and to create code and data isolation using underlying mcu mpu hardware protection mechanisms it is ultimately the responsibility of the device builder to ensure the device fully meets the evolving security requirements associated with its specific use case contribution please follow the instructions provided in the contributing md contributing md for the corresponding repository | azure-rtos embedded iot mcu microcontroller rtos exfat fat32 filesystem | os |
DeepUnfolding_WMMSE | deepunfolding wmmse this repository contains the entire code for our twc work iterative algorithm induced deep unfolding neural networks precoding design for multiuser mimo systems available at https ieeexplore ieee org stamp stamp jsp tp arnumber 9246287 and has been accepted for publication in twc for any reproduce further research or development please kindly cite our twc journal paper q hu y cai q shi k xu g yu and z ding iterative algorithm induced deep unfolding neural networks precoding design for multiuser mimo systems ieee trans wireless commun to be published doi 10 1109 twc 2020 3033334 requirements the following versions have been tested python 3 6 tensorflow 1 12 but newer versions should also be fine introductions there are three folders deepunfolding blackbox cnn and wmmse where deepunfolding corresponds to the proposed deep unfolding network in our paper blackbox cnn and wmmse are benchmarks deepunfolding training and testing run the main program train main py the introduction of each file train main py main program that implements the training and testing stages objective func py the sum rate loss function uw gradient py the gradients of the variables u and w in the last layer of the deep unfolding neural network uw conj gradient py the conjugate gradients of the variables u and w in the last layer of the deep unfolding neural network generate channel py generate the complex gaussian channels which could be modified to other channel models test model py import the trained model and test its performance blackbox cnn data prepareation firstly we run generate data m in the folder generatedata to generate the training dataset which consists of the inputs in the file input h csv and the labels in the file output uw csv the two files are generated in the folder mat and should be copied into the folder dataset training and testing run blackbox cnn py which generates four csv files in the folder dataset finally we run the file test predict m in the folder test to see the sum rate performance note that the file path in test predict m should be modified correspondingly wmmse run the main program wmmse py which implements the iterative wmmse algorithm | deep-learning-for-communications deep-unfolding beamforming-design multiuser-mimo-systems unfolded-wmmse | os |
Grocery-Pharmacy-Courier-Delivery-App-Backend-Driver-Vendor-app | p align center a href https laravel com target blank img src https raw githubusercontent com laravel art master logo lockup 5 20svg 2 20cmyk 1 20full 20color laravel logolockup cmyk red svg width 400 a p p align center a href https travis ci org laravel framework img src https travis ci org laravel framework svg alt build status a a href https packagist org packages laravel framework img src https img shields io packagist dt laravel framework alt total downloads a a href https packagist org packages laravel framework img src https img shields io packagist v laravel framework alt latest stable version a a href https packagist org packages laravel framework img src https img shields io packagist l laravel framework alt license a p about laravel laravel is a web application framework with expressive elegant syntax we believe development must be an enjoyable and creative experience to be truly fulfilling laravel takes the pain out of development by easing common tasks used in many web projects such as simple fast routing engine https laravel com docs routing powerful dependency injection container https laravel com docs container multiple back ends for session https laravel com docs session and cache https laravel com docs cache storage expressive intuitive database orm https laravel com docs eloquent database agnostic schema migrations https laravel com docs migrations robust background job processing https laravel com docs queues real time event broadcasting https laravel com docs broadcasting laravel is accessible powerful and provides tools required for large robust applications learning laravel laravel has the most extensive and thorough documentation https laravel com docs and video tutorial library of all modern web application frameworks making it a breeze to get started with the framework if you don t feel like reading laracasts https laracasts com can help laracasts contains over 1500 video tutorials on a range of topics including laravel modern php unit testing and javascript boost your skills by digging into our comprehensive video library laravel sponsors we would like to extend our thanks to the following sponsors for funding laravel development if you are interested in becoming a sponsor please visit the laravel patreon page https patreon com taylorotwell premium partners vehikl https vehikl com tighten co https tighten co kirschbaum development group https kirschbaumdevelopment com 64 robots https 64robots com cubet techno labs https cubettech com cyber duck https cyber duck co uk many https www many co uk webdock fast vps hosting https www webdock io en devsquad https devsquad com curotec https www curotec com op gg https op gg contributing thank you for considering contributing to the laravel framework the contribution guide can be found in the laravel documentation https laravel com docs contributions code of conduct in order to ensure that the laravel community is welcoming to all please review and abide by the code of conduct https laravel com docs contributions code of conduct security vulnerabilities if you discover a security vulnerability within laravel please send an e mail to taylor otwell via taylor laravel com mailto taylor laravel com all security vulnerabilities will be promptly addressed license the laravel framework is open sourced software licensed under the mit license https opensource org licenses mit | front_end |
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spruce | spruce https spruce mongodb com middot github license https img shields io badge license apache2 0 blue svg https github com evergreen ci spruce main license spruce is the react ui for mongodb s continuous integration software table of contents getting started getting started running locally running locally environment variables environment variables deployment deployment requirements requirements how to deploy how to deploy getting started running locally 1 clone the spruce github repository 2 ensure you have node js 16 installed 3 ask a colleague for their cmdrc json file and follow the instructions here environment variables 4 run yarn 5 start a local evergreen server by doing the following clone the evergreen repo run make local evergreen 6 run yarn run dev this will launch the app and point it at the local evergreen server you just ran storybook run yarn run storybook to launch storybook and view our shared components code formatting install the prettier code formatting plugin in your code editor if you don t have it already the plugin will use the prettierrc settings file found at the root of spruce to format your code gql query linting follow these directions to enable query linting during local development so your evergreen graphql schema changes are reflected in your spruce query linting results 1 symlink the standard definition language graphql schema used in your backend to a file named sdlschema in the root of the spruce directory to enable query linting with eslint like so ln s path to evergreen repo graphql schema sdlschema 2 run yarn run eslint to see the results of query linting in your terminal or install a plugin to integrate eslint into your editor if you are using vscode we recommend eslint by dirk baeumer environment variables env cmd https github com toddbluhm env cmd readme is used to configure build environments for production staging and development we use two files to represent these various environments env cmdrc local json for local builds with non sensitive information and env cmdrc json for builds deployed to s3 this file is git ignored because it contains api keys that we do not want to publish it should be named env cmdrc json and placed in the root of the project this file is required to deploy spruce to production and to staging the credential file is located in the r d dev prod 1password vault graphql type generation we use code generation to generate our types for our graphql queries and mutations when you create a query or mutation you can run the code generation script with the steps below the types for your query mutation response and variables will be generated and saved to gql generated types ts much of the underlying types for subfields in your queries will likely be generated there as well and you can refer to those before creating your own setting up code generation create a symlink from the schema folder from evergreen with the spruce folder using ln s path to evergreen repo graphql schema sdlschema using code generation from within the spruce folder run yarn codegen as long as your queries are declared correctly the types should generate code generation troubleshooting and tips queries should be declared with a query name so the code generation knows what to name the corresponding type each query and mutation should have a unique name since query analysis for type generation occurs statically we cant place dynamic variables with in query strings we instead have to hard code the variable in the query or pass it in as query variable common errors sometimes you may run into an error where a dependency is out of date or in a broken state if you run into this issue try running yarn install to reinstall all dependencies if that does not work try deleting your node modules folder and running yarn install again you can use the yarn clean command to do this for you testing spruce has a combination of unit tests using jest and integration tests using cypress unit tests unit tests are used to test individual features in isolation we utilize the jest test runner https jestjs io to execute our unit tests and generate reports there are 3 types of unit tests you may encounter in this codebase component tests these test react componenents we utilize react testing library https testing library com docs react testing library intro to help us write our component tests react testing library provides several utilities that are useful for making assertions on react componenents when writing component tests you should import test utils https github com evergreen ci spruce blob main src test utils index tsx instead of react testing library test utils is a wrapper around react testing library which provides a series of helpful utilities for common testing scenarios such as querybydatacy which is a helper for selecting data cy attributes or renderwithroutermatch which is helpful for testing components that rely on react router hook tests often times you may find yourself writing custom react hooks https reactjs org docs hooks custom html the best way to test these is using react hooks testing library https react hooks testing library com react hooks testing library allows you to test your custom hooks in isolation without needing to wrap them in a component it provides several methods that make it easy to assert and test different behaviors in your hooks such as waitfornextupdate https react hooks testing library com reference api waitfornextupdate which will wait for your hook to rerender before allowing a test to proceed standard utility tests these are the most basic of tests they do not require any special libraries to run and often just test standard javascript functions you can run all unit tests using yarn test you can run a specific unit test using yarn test t test name you can run jest in watch mode using yarn test watch this will open an interactive cli you can use to automatically run tests as you update them e2e tests at a high level we use cypress https www cypress io to start a virtual browser that is running spruce cypress then is able to run our test specs which tell it to interact with the browser in certain ways and makes assertions about what happens in the ui note that you must be running the evergreen server on localhost 9090 for the front end to work in order to run the cypress tests do the following assuming you have this repo checked out and all the dependencies installed by yarn 1 start the evergreen back end with the sample local test data you can do this by typing make local evergreen in your evergreen folder 2 start the spruce local server by typing yarn build local yarn serve in this repo 3 run cypress by typing one of the following yarn cy open opens the cypress app in interactive mode you can select tests to run from here in the cypress browser yarn cy run runs all the cypress tests at the command line and reports the results yarn cy test cypress integration hosts hosts filtering ts runs tests in a specific file at the command line replace the final argument with the relative path to your test file snapshot tests snapshot tests are automatically generated when we create storybook stories these tests create a snapshot of the ui and compare them to previous snapshots which are stored as files along side your storybook stories in a snapshots directory they try to catch unexpected ui regressions read more about them here https jestjs io docs snapshot testing how to get data for your feature if you need more data to be able to test out your feature locally the easiest way to do it is to populate the local db using real data from the staging or production environments 1 you should identify if the data you need is located in the staging or prod db and ssh into them you should be connected to the office network or vpn before proceeding the urls for these db servers can be located in the fabfile py located in the evergreen directory or here https github com 10gen kernel tools blob master evergreen fabfile py 2 you should ensure you are connected to a secondary node before proceeding 3 run mongo to open the the mongo shell 4 identify the query you need to fetch the data you are looking for mci secondary rs secondaryok allows read operations on a secondary node mci secondary use mci use the correct db switched to db mci mci secondary db distro find id archlinux small the full query 5 exit from the mongo shell and prepare to run mongoexport mongoexport db mci collection distro out distro json query id archlinux small 2020 07 29t17 41 50 266 0000 connected to localhost 2020 07 29t17 41 50 269 0000 exported 1 record after running this command a file will be saved to your home directory with the results of the mongoexport note you may need to provide the full path to mongoexport on the staging db var lib mongodb mms automation mongodb linux x86 64 4 0 5 bin mongoexport db mci collection distro out distro json query id archlinux small 2020 07 29t17 41 50 266 0000 connected to localhost 2020 07 29t17 41 50 269 0000 exported 1 record 6 exit the ssh session using exit or ctrl d 7 you can now transfer this json file to your local system by running the following command scp db you sshed into distro json this will save a file named distro json to the current directory 8 you should run this file through the scramble eggs script to sanitize it and remove any sensitive information make scramble file path to file json from within the evergreen folder 9 once you have this file you can copy the contents of it to the relevant testdata local collection json file with in the evergreen folder 10 you can then delete bin load local data within the evergreen folder and run make local evergreen to repopulate the local database with your new data notes when creating your queries you should be sure to limit the amount of documents so you don t accidently export an entire collection you can do this by passing a limit number flag to mongoexport logkeeper spruce has a minimal dependency on logkeeper it is used for cypress tests on the job logs page if you d like to get set up to develop these tests complete the following 1 clone the logkeeper repository https github com evergreen ci logkeeper 2 run yarn bootstrap logkeeper to download some sample resmoke logs from s3 3 run the command output by the previous step to seed the env variables and start the local logkeeper server at http localhost 8080 deployment requirements you must be on the main branch if deploying to prod a cmdrc json file is required to deploy because it sets the environment variables that the application needs in production and staging environments see environment variables environment variables section for more info about this file how to deploy run one of the following commands to deploy to the appropriate environment 1 yarn deploy prod deploy to https spruce mongodb com 2 yarn deploy staging deploy to https spruce staging corp mongodb com 3 yarn deploy beta deploy to https spruce beta corp mongodb com beta connects to the production backend in case of emergency i e evergreen github or other systems are down a production build can be pushed directly to s3 with yarn deploy prod local | front_end |
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nnmnkwii | alt text assets logo wide png nnmnkwii nanamin kawaii docs stable img docs stable url docs latest img docs latest url pypi https img shields io pypi v nnmnkwii svg https pypi python org pypi nnmnkwii python package https github com r9y9 nnmnkwii actions workflows ci yaml badge svg https github com r9y9 nnmnkwii actions workflows ci yaml build status https app travis ci com r9y9 nnmnkwii svg branch master https app travis ci com r9y9 nnmnkwii codecov https codecov io gh r9y9 nnmnkwii branch master graph badge svg https codecov io gh r9y9 nnmnkwii doi https zenodo org badge 96328821 svg https zenodo org badge latestdoi 96328821 library to build speech synthesis systems designed for easy and fast prototyping documentation stable docs stable url mdash most recently tagged version of the documentation latest docs latest url mdash in development version of the documentation installation the latest release is availabe on pypi assuming you have already numpy installed you can install nnmnkwii by pip install nnmnkwii if you want the latest development version run pip install git https github com r9y9 nnmnkwii or git clone https github com r9y9 nnmnkwii cd nnmnkwii python setup py develop or install this should resolve the package dependencies and install nnmnkwii property at the moment nnmnkwii autograd package depends on pytorch http pytorch org if you need autograd features please install pytorch as well acknowledgements the library is inspired by the following open source projects merlin https github com cstr edinburgh merlin librosa https github com librosa librosa docs latest img https img shields io badge docs latest blue svg docs latest url https r9y9 github io nnmnkwii latest docs stable img https img shields io badge docs stable blue svg docs stable url https r9y9 github io nnmnkwii stable logo was created by gloomy ghost 740291272 https github com 740291272 40 https github com r9y9 nnmnkwii issues 40 | machine-learning speech-synthesis voice-conversion python text-to-speech speech-processing | os |
beamcommunity.github.com | beamcommunity github com this is the repository that holds the source code for the http beamcommunity github io website the site of the beam community the google summer of code mentoring organisation for projects that use the erlang vm also known as the beam | erlang beam elixir ejabberd nerves zotonic | os |
dart_web_toolkit | dart web toolkit or dwt dart web toolkit dwt is a development toolkit for building and optimizing complex browser based applications build status https drone io github com akserg dart web toolkit status png https drone io github com akserg dart web toolkit latest license copyright c 2012 2015 sergey akopkokhyants licensed under the apache 2 0 license credits this makes use of a lot of ideas from the gwt source code so big thanks to the google gwt team | front_end |
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CG2271 | cg2271 2021 s1 this repository contains all the code used for this iteration of cg2271 lab and project code used are located in their respective folders project the project specifications can be found here https github com r ramana cg2271 blob master project documents project 20specifications ay2021sem1 pdf the project report can be found here https github com r ramana cg2271 blob master project report pdf click the image below for a short video on our project project video https img youtube com vi o zyy2m2vnm 0 jpg https www youtube com watch v o zyy2m2vnm the aim of the project is to design a real time operating system rtos based robotic car that will be controlled through an android app the robotic car must be able to fulfil the following features 1 establish a bt connection with the android app 2 receive commands from the android app and execute the correct response 3 move the car in multiple directions 4 control the various led s according to the car s status 5 play different sounds tunes according to the cars status features bt connectivity x flash green led to indicate connection x receive specific data from bt06 then start led and play tone x create function to receive data from app x call flash green led x call play tune motor control x move in all 4 directions x have function for each direction x control all 8 pins for each direction x tpm pwm initialize all 8 pins using library functions x choose which pins belong to which wheel x create function to configure each wheel x create function to configure movement using function for wheels x create function to receive data from app and choose direction led control x green leds running mode when moving x green led all lit when stationary x red led flashing 1 hz when moving x red led flashing 2 hz when stationary x gpio initialize all 11 pins using library functions x abstract gpio initialization x create function to control gpio pins x create function for running mode vs all lit mode x create function for 1 hz and 2 hz audio control x continuously play song from start to end x play unique tone at the end x tpm pwm initialize 1 pin using library functions x create function to play tune running vs tune end | os |
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AINote | ai ai ai ai gpt 4 langchain aigc langchain aigc https github com shengqinyang openai quickstart fork from https github com djangopeng openai quickstart 1 openai api api project openai api 2 openai api api openaitranslator https github com shengqinyang openai quickstart tree main openai translator fork from https github com djangopeng openai quickstart tree main openai translator 3 langchain langchain note 8 langchain md langchain langchain langchain langchain langchain 5 project langchain model i o models prompt output parsers chains chain memory read write data connection agents chatgpt google langchain 5 resource ai langchain xmind 4 langchain openaitranslator note 9 openaitranslator langchain md langchain openaitranslator project langchain openai translator 5 autogpt note 10 langchain autogpt md autogpt langchain autogpt serpapiwrapper filetool vectorstores embeddings chat models langchain autogpt project langchain autogpt 6 sales consultant note 11 sales consultant md sales consultant sales consultant sales consultant indexes qa chain example selectors langchain sales consultant project langchain sales chatbot 7 chatglm2 6b int4 project langchain model io model chatglm ipynb apache https www apache org licenses license 2 0 jean open main 163 com | aigc langchain llm openai promp prompt | ai |
ES-SW-Design-MC | es sw design mc design tasks for embedded systems software design master class program by sprints | os |
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Pretrain-Vision-and-Large-Language-Models-in-Python | packt conference put generative ai to work on oct 11 13 virtual https packt link jgiey b p align center packt conference https hub packtpub com wp content uploads 2023 08 put generative ai to work packt png https packt link jgiey p b 3 days 20 ai experts 25 workshops and power talks code b usd75off b pretrain vision and large language models in python a href https www packtpub com product pretrain vision and large language models in python 9781804618257 img src https m media amazon com images i 41hiraoozwl sx403 bo1 204 203 200 jpg alt data engineering with aws height 256px align right a this is the code repository for pretrain vision and large language models in python https www packtpub com product pretrain vision and large language models in python 9781804618257 published by packt end to end techniques for building and deploying foundation models on aws what is this book about foundation models have forever changed machine learning from bert to chatgpt clip to stable diffusion when billions of parameters are combined with large datasets and hundreds to thousands of gpus the result is nothing short of record breaking the recommendations advice and code samples in this book will help you pretrain and fine tune your own foundation models from scratch on aws and amazon sagemaker while applying them to hundreds of use cases across your organization with advice from seasoned aws and machine learning expert emily webber this book helps you learn everything you need to go from project ideation to dataset preparation training evaluation and deployment for large language vision and multimodal models with step by step explanations of essential concepts and practical examples you ll go from mastering the concept of pretraining to preparing your dataset and model configuring your environment training fine tuning evaluating deploying and optimizing your foundation models you will learn how to apply the scaling laws to distributing your model and dataset over multiple gpus remove bias achieve high throughput and build deployment pipelines by the end of this book you ll be well equipped to embark on your own project to pretrain and fine tune the foundation models of the future this book covers the following exciting features find the right use cases and datasets for pretraining and fine tuning prepare for large scale training with custom accelerators and gpus configure environments on aws and sagemaker to maximize performance select hyperparameters based on your model and constraints distribute your model and dataset using many types of parallelism avoid pitfalls with job restarts intermittent health checks and more evaluate your model with quantitative and qualitative insights deploy your models with runtime improvements and monitoring pipelines if you feel this book is for you get your copy https www amazon com pretrain vision language models beginners dp 180461825x today following is what you need for this book if you re a machine learning researcher or enthusiast who wants to start a foundation modelling project this book is for you applied scientists data scientists machine learning engineers solution architects product managers and students will all benefit from this book intermediate python is a must along with introductory concepts of cloud computing a strong understanding of deep learning fundamentals is needed while advanced topics will be explained the content covers advanced machine learning and cloud techniques explaining them in an actionable easy to understand way software and hardware list chapter software required os required 1 15 aws web services aws with a recent version of a modern web browser chrome edge etc any os get to know the author emily webber is a principal machine learning ml specialist solutions architect at amazon web services aws in her more than five years at aws she has assisted hundreds of customers on their journey to ml in the cloud specializing in distributed training for large language and vision models she mentors ml solution architects authors countless feature designs for sagemaker and aws and guides the amazon sagemaker product and engineering teams on best practices in regard to ml and customers emily is widely known in the aws community for a 16 video youtube series https www youtube com playlistlist plhr1kzpdzukcor 6j zmsrvynlut qszz featuring sagemaker with 211 000 views and for giving a keynote speech at o reilly ai london 2019 on a novel reinforcement learning approach she developed for public policy note from author hi there if you d like some examples to get hands on with pretraining your own foundation models then you ve come to the right place this repository is meant to pair with my 2023 book on the topic https bit ly dist train book providing an end to end guide for foundation models on aws the book has detailed explanations and relevant examples of key topics in the lifecycle of foundation models including where they come from why you d design your own how to do that and how to build it into an application this repository contains examples to help you implement that guidance at scale eventually i ll have some net new examples here for you to follow step by step across the whole lifecycle to get you moving between now and then you ll find in this repository links to hands on examples elsewhere that explain how to implement everything you learned about in the book i ll start uploading these in july and will be pushing them out slowly the concepts theory and examples discussed in the book are general and extend to any compute environment however all of the hands on guidance will focus explicitly on aws and especially amazon sagemaker so let s get rolling book structure the book is broken down into 15 chapters let s recap those here 1 introduction to pretraining foundation models 2 picking the right use case and dataset 3 picking the right model 4 prepping your containers and accelerators on aws 5 distribution fundamentals data and model parallel 6 building a distributed data loader 7 finding the right hyperparameters 8 large scale training on amazon sagemaker 9 advanced training concepts 10 fine tuning and evaluating models 11 detecting and mitigating bias 12 deploying your model on sagemaker 13 prompt engineering 14 mlops for foundation models on aws 15 future trends in pretraining foundation models you don t need coding examples for chapters 1 and 15 because those are straightforward and high level that means you ve got 12 more chapters to work through i ll break these up into 3 groups for you preparing your environment training and deploying your repository structure will then look like this repository structure i preparation https github com packtpublishing pretrain vision and large language models in python tree main preparation dataset analysis model analysis preparing your containers and accelerators on aws basics of distribution ii training https github com packtpublishing pretrain vision and large language models in python tree main training distributed data loader hyperparameters training foundation models on sagemaker compilation and throughput fine tuning and evaluating iii deployment https github com packtpublishing pretrain vision and large language models in python tree main deployment bias detection and mitigation hosting foundation models on sagemaker prompt engineering mlops each of these directories will start with just a list of links to helpful hands on tutorials to dive into the content in july i ll update these with new examples for pretraining in python across vision and language on aws happy trails asking questions and getting help if you re stuck feel free to log an issue in the repo here and i ll try to address it you can also reach me on twitch i m live every monday at 9am pst 12pm est 6pm cet right here https www twitch tv aws schedule bring your question and come hang out with us the show is called build on generative ai with myself and darko mesaros you can also ping me on linkedin right here https www linkedin com in emily webber 921b4969 | ai |
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textbook-analysis | textbook analysis via natural language processing code for the paper content analysis of textbooks via natural language processing lucy l demszky d bromley p jurafsky d 2019 content analysis of textbooks via natural language processing findings on gender race and ethnicity in texas us historytextbooks under review indicates equal contribution these scripts are for those who would like to perform analyses on their own textbook data or any other data to better understand the representation of minorities and women in text you do not need any technical background to run these analyses see our paper for a more detailed description of each method first download this repository by running the following in the terminal and then enter the directory git clone https github com ddemszky textbook analysis git cd textbook analysis create and activate virtual environment python3 m venv env source env bin activate install all required packages pip install r requirements txt python m spacy download en core web sm install spacy models for english our scripts were written for the studies described in our paper it s likely you might be researching other research questions about other groups of people for example if you were analyzing differences in the descriptors of politicians pre 1850 and post 1850 you could run the files in our toolkit using a list of people terms categorized based on that comparison email us if you want some advice for your specific use case for tutorials conda deactivate deactivate the base needed for new macs pip install jupyter python m ipykernel install user name env jupyter notebook data format first prepare your data such that each textbook is in a separate text file simple txt in the same directory perform any clean ups that you think might be necessary e g removing characters that you do not want remove short lines etc try to ensure complete sentences are on the same line in some cases digitization may split sentences across lines and removing paratext e g table of contents glossaries index can help the analysis focus on the main content counting the mentions of people pre processing in order to count the number of times people are mentioned it s important to first run co reference resolution on the data so that pronouns for example are substituted by the nouns that they refer to run the script below replacing the input and output directories with your paths python run coref py input dir data source txts output dir data coref resolved txts note that this script may take a while to run on large files it took 1hr on our 15 textbooks using my local machine counting the mentions of demographic groups to count the frequency of mentions for different groups of people e g different genders run the following python count mentions py input dir data coref resolved txts output dir results people terms wordlists people terms csv we include a people terms csv file in wordlists but you can replace it with your own file the format of this file should be the following it should have 3 columns separated by a comma the first including a word phrase referring to people lowercase the second should be the demographic group that the word phrase belongs to and the third is the type of demographic if a word belongs to multiple demographic groups then add that as a separate line for example bridesmaid woman gender african black race ethnicity latina latinx race ethnicity latina woman gender our script takes unmarked terms such as farmer and if they are modified by a marker such as black farmer recategorizes the instance of farmer to the category black an output file people mentions csv will be generated in the output directory the file will have the following columns source text the name of the text file corresponding to a textbook for example demographic the demographic category count the number of terms belonging to that demographic in the given source text the above script works for unigrams single words but not yet for bigrams or trigrams phrases if a word contains a e g asian american it is considered a trigram we are hoping to offer broader support for this in the future counting the mentions of named people to count the frequency of mentions for named people e g eleanor roosevelt you first need to run named entity recognition ner the following script will run ner on your files and it will also combine last names with the most recent full name in the data it will also output a new dataset in ner dir where named entities have standardized wikidata names e g franklin d roosevelt franklin delano roosevelt python count names py input dir data coref resolved txts ner dir data ner coref txts output dir results named people this script will generate separate files for each textbook in the specified output directory with counts of each named individual it will also generate a file full2wikiname json which saves aliases already queried from wikidata allows you to rerun the script faster if you would also like to obtain demographic information for the named individuals automatically you can run the following script this script builds on wikidata which has a lot of missing information e g it usually doesn t specify race for white people but it has high coverage of gender information for example the input should be the output of count names py names will be matched based on wikidata aliases python get wikidata attributes py input dir results named people output dir results the output file will be a csv where the first column is the named entity as found in the text and the rest of the columns correspond to wikidata attributes including gender race ethnicity and occupation if the person was not found in the database or the category was not listed the value will be none note that the same person may show up multiple times if multiple wikidata names are matched with it looking at how people are described verbs and adjectives one way to understand how people are described is to look at the verbs and adjectives that they co occur with for this you first need to run dependency parsing you can run the following script where the format of the people terms file should be the way it is described above our paper used dozat et al 2017 s dependency parser since many of the other tools in this repo are based on spacy and it is time consuming to train a parser from scratch the script we provide here uses spacy s dependency parser python get descriptors py input dir data ner coref txts output dir results people terms wordlists people terms csv this script will output people descriptors csv in the output directory with the following columns source text the name of the text file corresponding to a textbook for example people term the specific people term category the demographic category or named for named people word the verb adjective pos the part of speech tag for the word adj or verb relation the dependency parsing relation note that terms associated with multiple demographic categories would be listed multiple times for example black woman would be listed under both black and women the verbs include those that the person is performing nsubj and those that are performed on the person dobj the current implementation also obtains verbs and adjectives for named individuals based on the output of the most popular named people of count names py log odds ratio we can look at which words are significantly more associated with one group vs another group based on word counts monroe et al 2009 the group1 and group2 arguments are two groups of labels comma separated that you want to compare for example the command below compares common nouns referring to women with all other categories we have for common nouns if a descriptor falls under multiple labels cases of intersectionality it is included in the first group but not the second for example the script handles words referring to women that are marked by ethnicity by not including them in the second group python run log odds py input file results people descriptors csv output dir results group1 women group2 men other other minority white black hispanic latinx the output file is log odds txt in the results folder note that you need quotation marks if the input argument has a space in it power agency and sentiment using external lexicons to quantify the affective connotations of words associated with people we use two lexicons in our paper the first the nrc valence arousal and dominance vad lexicon https saifmohammad com webpages nrc vad html contains a more than 20 000 english words download the zip file on their website and unzip it scores for words can be found in nrc vad lexicon txt in the unzipped folder you will want to add a line to the top of the file that says word valence arousal dominance where each word is separated by a tab this allows our reader to use the file the second the connotation frames dataset https homes cs washington edu hrashkin connframe html and power and agency https maartensap com connotation frames frames click on the full labelled english connotation frame data set link in the first link and the download the verbs link in the second once these downloads are unzipped sentiment frames can be found in the file labeled full frame info txt and agency and power frames are in the file agency power csv place these three lexicons full frame info txt nrc vad lexicon txt and agency power csv in the wordlists folder python get lexicon averages py input file results people descriptors csv output dir results the script outputs a csv file in the output directory called lexicon output csv with the following columns demographic the demographic category dimension name of the dimension score score for the particular dimension confidence interval 95 confidence interval for the score look at common verbs or adjectives associated with a category and their scores for some dimension python get lexicon averages py input file results people descriptors csv output dir results inspect true category black score type agency note that for power the printed score is positive if the verb is power agent negative if power theme and the provided function does not take in account in which direction it is usually associated with the category of people measure association between words via word embeddings another way to measure association between words is to represent words as vectors and look at their distance in the vector space for this you first need to create vectors for the words in your text you can do so by running the following script note that this will take a while depending on your data size and number of runs you want to do python run word2vec py input dir data final txts output dir data word2vec models num runs 50 dim 100 bootstrap the input dir argument should be a directory that includes the txt files the script will create separate model files for each training run in output dir by default the script runs 50 separate bootstrap training runs this means that we sample from the sentences with replacement each time we train the model this method as found by antoniak and mimno 2018 https mimno infosci cornell edu info3350 readings antoniak pdf ensures that we can measure word associations robustly and calculate significance values you can decrease the number of runs if you do not want to use bootstrapping you can set the number of runs to 1 and remove the bootstrap argument you can also change dimension size of the embeddings by default it s set to 100 if you decrease it you might get slightly lower quality embeddings but they will take up less space if you increase it you might get embeddings that capture more subtle semantics but they will take up more space to get the most closely associated word with a particular group run the following script here words is the comma separated list of seed words that refer to that group or concept the script will print out the top closest words and their mean cosine similarity across all model runs python word2vec get closest py words woman women she her hers word2vec dir data word2vec models if you want to compare the similarity of words from various topics to two sets of terms e g terms referring to men vs women follow the following steps 1 create a dictionary file of terms referring to different themes such as the one in wordlists liwc queries json in the following format if you only have one category that s fine too but you still need to follow the same format home home domestic household chores family work work labor workers economy trade business jobs company industry pay working salary wage achievement power authority achievement control took control won powerful success better efforts plan tried leader 2 create two lists of words one for one group of interest e g women and the other for the other group of interest e g men you can see examples in wordlist woman terms txt and wordlist man terms txt 3 run the following script substituting the file references with your own paths change the arguments for name1 and name2 with your group names python word2vec calculate similarity py queries wordlists liwc queries json words1 wordlists woman terms txt words2 wordlists man terms txt name1 women name2 men word2vec dir data word2vec models output file results word2vec cosines csv the script will save a dataframe in the file specified by output file with the following columns name1 mean cosine similarity of query word to words in group 1 name2 mean cosine similarity of query word to words in group 2 query query word word category category that word belongs to p value p value for cosine similarity calculated using two tailed t test analyzing topics to induce topics in your data you first need to run a topic model our script runs lda using the very efficient mallet package in order to run this you first need to download mallet download and unzip the most recent mallet package here http mallet cs umass edu download php the following script runs the topic model python get topics py mallet dir users your username mallet 2 0 8 bin num topics 70 input dir data coref resolved txts output dir topics topics 70 stem where the arguments are the following mallet dir directory of the mallet binary file num topics number of topics to induce input dir directory of textbook files output dir output directory for the topic model stem whether to stem words before running the topic model in the paper we do the script will save the model and all associated files e g vocabulary in output dir and it will also create separate files for each book you can inspect the topics in output dir topic names json which contains the top 10 highest probability terms for each topic note that this script runs the topic model on all books at once in input dir so if you want to get separate topic models for each book then you should only include the relevant books in input dir if you want to run a topic model on all books and then separate the topic distributions per book afterwards this is what we did you can do that with the script below topic prominence you can obtain the number of sentences where each topic is prominent above a ratio of 1 as determined by the topic model using the following script python get topic prominence py topic dir topics textbook dir data coref resolved txts where the arguments are the following topic dir directory containing the topic files textbook dir directory containing the textbook files for the purposes of obtaining titles the script will generate a dataframe with the following columns book title of the book topic id id of the topic as in topics topic names json topic words top words associated with the topic as in topics topic names json raw count raw number of sentences where the topic is prominent for the given book topic proportion the proportion of sentences where the topic is prominent for the given book | ai |
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EngSQL | engsql sql clr libraries for process simulation and fast storage versioning and querying of 2d 3d point cloud and engineering documents using f python r sql server filestream and sql server temporal tables | cloud |
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FedCV | fedcv has been migrated to https github com fedml ai fedml tree master python app fedcv | computer-vision federated-learning image-segmentation medical-imaging object-detection image-classifiation | ai |
phormatics | this repo is not actively maintained and may not work out of the box as it has been 3 years since the last update if you want to learn more about the next version of this project check it out here https www youtube com watch v tzcrycjtwwa h1 phormatics em using ai to maximize your workout em h1 img src https github com jrobchin phormatics blob master screenshots frontpage gif raw true height 250px br sup em f1 front page the gif may be choppy at first but it s worth it i promise em sup by jason chin a href https linkedin com in jrobchin img src https raw githubusercontent com jrobchin phormatics master screenshots linkedin png height 20px a a href https github com jrobchin img src https raw githubusercontent com jrobchin phormatics master screenshots github png height 20px a charlie lin a href https www linkedin com in charlielin10 img src https raw githubusercontent com jrobchin phormatics master screenshots linkedin png height 20px a a href https l facebook com l php u https 3a 2f 2fgithub com 2fcharlielin99 h atowbl6a7wzojrslqemob8lnd5qqhnhcdu2wzfa2gepfggdx2nld izgnmx ybgada5tsql483yueldkehhcod hxt6udabdplbasmxtmbldlh291 wb6jjz1uoiqq img src https raw githubusercontent com jrobchin phormatics master screenshots github png height 20px a brad huang a href https linkedin com in brad huang img src https raw githubusercontent com jrobchin phormatics master screenshots linkedin png height 20px a a href https github com bradhuang1999 img src https raw githubusercontent com jrobchin phormatics master screenshots github png height 20px a calvin woo a href https www linkedin com in cwoozle img src https raw githubusercontent com jrobchin phormatics master screenshots linkedin png height 20px a a href https github com cwoozle img src https raw githubusercontent com jrobchin phormatics master screenshots github png height 20px a hacknyu2018 http hacknyu org project developed in 36 hours focusing on using a i and computer vision to build a virtual personal fitness trainer capable of using 2d human pose estimation with commodity web cameras to critique your form and count your repetitions this project won the award for the most startup viable hack as awarded by contrary capital https contrarycap com 2d human pose estimation img src https github com jrobchin phormatics blob master screenshots usage png raw true height 335px img br sup em f2 live pose estimation in a busy environment note here the user has over extended their right arm image is mirrored which is considered bad form in this variant of the dumb bell shulder press hence the message em sup the pose estimation was based off of tf pose estimation https github com ildoonet tf pose estimation by ildoonet https github com ildoonet the model architecture openpose https github com cmu perceptual computing lab openpose developed by cmu perceptual computing lab https www cmu edu consists of a deep convolutional neural network for feature extraction mobilenet https arxiv org abs 1704 04861 and a two branch multi stage cnn for confidence maps and part affinity fields pafs this feature allowed us to track the position of the user s joints using a commodity webcam data flow web based img src https github com jrobchin phormatics blob master notes dataflow diagram jpg raw true img sup em f3 pseudo data flow diagram note the pose estimation model output must be processed as it returns pose estimation for all possible humans in frame see future changes sup 1 sup https github com jrobchin phormatics future changes em sup this app runs in browser and the pose estimation and form critique generation is performed on a flask http flask pocoo org server the webcam feed is captured using webrtc https webrtc org and screenshots are sent to the server as a base64 encoded string every 50ms or as fast as the server can respond which ever is slower see future changes sup 2 sup https github com jrobchin phormatics future changes this means the server could be run in the cloud on high performance hardware and the client could be any device with a webrtc supported web browser and camera there is also the option for video to be recorded and sent to the server for post processing if the user s network connectivity is too slow to stream a live feed currently supported exercise analysis squat exaggerated knees forward checking dumbbell shoulder press exaggerated arm bend and extension checking bicep curls horizontal elbow deviation from shoulder checking future changes 1 multiple pose estimations for one user current the model estimates joints for all subjects found in the input image we then analyze the output and extract the pose that is most likely to be the user possible improvements a modify model and training data to only estimate a single best pose or b implement re identification and support multiple users at once this is viable as forward propagation time does not increase with multiple poses being estimated 2 webcam image data transfer current webcam captures are encoded in base64 strings and a post request is sent to the server with the data note this was done for ease of implementation due to the hackathon time constraint possible improvements implement web sockets to transfer webcam captures instead | computer-vision fitness python opencv tensorflow pose estimation human model webrtc camera keras recognition classification artificial-intelligence neural-network data-science flask | ai |
.config | actions openwrt license https img shields io github license mashape apistatus svg style flat square label license https github com p3terx actions openwrt blob master license github stars https img shields io github stars p3terx actions openwrt svg style flat square label stars logo github github forks https img shields io github forks p3terx actions openwrt svg style flat square label forks logo github build openwrt using github actions read the details in my blog in chinese https p3terx com archives build openwrt with github actions html usage click the use this template https github com p3terx actions openwrt generate button to create a new repository generate config files using lean s openwrt https github com coolsnowwolf lede source code you can change it through environment variables in the workflow file push config file to the github repository and the build starts automatically progress can be viewed on the actions page when the build is complete click the artifacts button in the upper right corner of the actions page to download the binaries tips it may take a long time to create a config file and build the openwrt firmware thus before create repository to build your own firmware you may check out if others have already built it which meet your needs by simply search actions openwrt in github https github com search q actions openwrt add some meta info of your built firmware such as firmware architecture and installed packages to your repository introduction this will save others time acknowledgments microsoft https www microsoft com microsoft azure https azure microsoft com github https github com github actions https github com features actions tmate https github com tmate io tmate mxschmitt action tmate https github com mxschmitt action tmate csexton debugger action https github com csexton debugger action cisco https www cisco com openwrt https github com openwrt openwrt lean s openwrt https github com coolsnowwolf lede cowtransfer https cowtransfer com wetransfer https wetransfer com mikubill transfer https github com mikubill transfer license mit https github com p3terx actions openwrt blob master license p3terx | server |
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view_components | p align center img width 300px alt primer viewcomponents logo src static assets view components svg p h1 align center primer viewcomponents h1 p align center viewcomponents for the primer design system p note this library is under active pre 1 0 development breaking changes are likely in patch releases documentation visit https primer style view components https primer style design components to view documentation license the gem is available as open source under the terms of the mit license https opensource org licenses mit | viewcomponents primer ruby rails design-system viewcomponent | os |
CriticsCorner | criticscorner project for development for internet and mobile apps course | front_end |
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lowPowerFreeRtos | low power sample project for stm32l15x series ready to go with stm32l152discovery and gcc freertos port for stm32l152 uc with low power features this repo shows basic project configuration and can be a start point for creating real world application for stm32l1xx based embedded systems it also contains high performed configuration for peripherals like i2c spi flash usart and usb so each possible peripherals application is tested and developed at stm32l152discovery board but it can be used as well as on custom board with stm32l15x chip also can use chips that are in driver section why has this repo in recent years i had a lot of projects especially for low power consumption electronic devices every time when i started a new project i was trying to figure out past mistakes from past projects and how to omit them in a new project also my goal was to don t make overengineering as well as follow kiss keep it simple stupid guidelines as an effect i created a repository for stm32l1xx uc which i pull every time when i have to write a new project rather than gathering whole code from scratch if you ever wrote embedded system based on small microcontroller you probably know that whole system is the same application there is no separation between os libc drivers and your code in this repository i try to keep clean separation between these components because i have see a lot of complication because of keeping different layers of software as a spaghetti my intention is has ready to go code that collects together rtos fs drivers your drivers vendors drivers 3rd party drivers trying to create layers of insulation for the individual components has the possibility of development and evolution over time anyone can use and save time how to use application is dedicated to run wit open source tools and fully tested so if you have gcc arm toolchain and openocd you can easily build it and debug using in example embedded st link with stm32l discovery demo board or any other first step download and install toolchain for cortex m3 uc i strongly recommend bare metal toolchain for cortex r m and cortex a it can be download at https launchpad net gcc arm embedded 4 7 4 7 2013 q3 update second clone this repo git clone https github com skirki lowpowerfreertos last thing come to project dir and type make make because the project is simple one gnu makefile it will compile your project and you are ready to go next things to flash your uc download open ocd and you can create and develop real world low power embedded systems project structure and best practices currently this demo project contains followed branches applications master empty ready to go project with all needed stuff usb easy sample configuration of usb end device serial port warning needed some hardware fixes on board more details in branch readme tmp sample smart thermometer application that uses externals rtc mcp9800 accelerometer lis35de and rtc m41t56c64 and show data on discovery onboard lcd about codding standards for details of embedded c development please read codingstandards at project root structure of project and dirs descriptions project root application your application and code logic is here nowhere else configuration bsp config sys config main c and so on this part is platform dependent if you want to port from discovery to another board you should only modify code in this folder drivers for dealing with external silicon your drivers should go here fatfs 3party software file system freertos 3party software task scheduler in example freertos debug debuger scripts ex open ocd or production code hdr shortcut for definition of registers for high performance and low memory cons peripherals your on chip peripherials code should go here i2c usart spi flash and so on | os |
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banglanmt | bangla nmt this repository contains the code and data of the paper titled not low resource anymore aligner ensembling batch filtering and new datasets for bengali english machine translation https www aclweb org anthology 2020 emnlp main 207 published in proceedings of the 2020 conference on empirical methods in natural language processing emnlp 2020 november 16 november 20 2020 updates the base translation models are now available for download the training code has been refactored to support opennmt py 2 2 0 https github com opennmt opennmt py colab notebook https colab research google com drive 1tpkyxewrf dujq 1qpapkrelc7joug9e usp sharing added for the inference module table of contents bangla nmt bangla nmt updates updates table of contents table of contents datasets datasets models models dependencies dependencies segmentation segmentation batch filtering batch filtering training evaluation training evaluation license license citation citation datasets download the dataset from here https docs google com uc export download id 1fllc0nnxfkvgavm3 cyw xex8p8ev3wm this includes our original 2 75m training corpus 2 75m preprocessed training preprocessing training corpus data risingnews dev test sets data preprocessed sipc dev test sets data sentencepiece vocabulary models for bengali and english vocab models the base sized transformer model 6 layers 8 attention heads checkpoints can be found below bengali to english https dl orangedox com g5euvqciot3uhofuzk english to bengali https dl orangedox com cxsei81fwvvzdnfpew sentencepiece vocabulary files vocab tar bz2 to evaluate these models on new datasets please refer to here https github com csebuetnlp banglanmt tree master training dependencies python 3 7 3 pytorch 1 2 http pytorch org cython https pypi org project cython faiss https github com facebookresearch faiss fastbpe https github com glample fastbpe sentencepiece https github com google sentencepiece install cli transliterate https pypi org project transliterate regex https pypi org project regex torchtext https pypi org project torchtext pip install torchtext 0 4 0 sacrebleu https pypi org project sacrebleu aksharamukha https pypi org project aksharamukha segmentation see segmentation module segmentation batch filtering see batch filtering module batch filtering training evaluation see training and evaluation module training try out the models in google colaboratory https colab research google com drive 1tpkyxewrf dujq 1qpapkrelc7joug9e usp sharing license contents of this repository are licensed under creative commons attribution noncommercial sharealike 4 0 international license cc by nc sa 4 0 https creativecommons org licenses by nc sa 4 0 citation if you use this dataset or code modules please cite the following paper inproceedings hasan etal 2020 low title not low resource anymore aligner ensembling batch filtering and new datasets for b engali e nglish machine translation author hasan tahmid and bhattacharjee abhik and samin kazi and hasan masum and basak madhusudan and rahman m sohel and shahriyar rifat booktitle proceedings of the 2020 conference on empirical methods in natural language processing emnlp month nov year 2020 address online publisher association for computational linguistics url https www aclweb org anthology 2020 emnlp main 207 doi 10 18653 v1 2020 emnlp main 207 pages 2612 2623 abstract despite being the seventh most widely spoken language in the world bengali has received much less attention in machine translation literature due to being low in resources most publicly available parallel corpora for bengali are not large enough and have rather poor quality mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation and also because of a high volume of noise present in them in this work we build a customized sentence segmenter for bengali and propose two novel methods for parallel corpus creation on low resource setups aligner ensembling and batch filtering with the segmenter and the two methods combined we compile a high quality bengali english parallel corpus comprising of 2 75 million sentence pairs more than 2 million of which were not available before training on neural models we achieve an improvement of more than 9 bleu score over previous approaches to bengali english machine translation we also evaluate on a new test set of 1000 pairs made with extensive quality control we release the segmenter parallel corpus and the evaluation set thus elevating bengali from its low resource status to the best of our knowledge this is the first ever large scale study on bengali english machine translation we believe our study will pave the way for future research on bengali english machine translation as well as other low resource languages our data and code are available at https github com csebuetnlp banglanmt | bangla-nlp machine-translation parallel-corpus parallel-corpora neural-machine-translation bangla-dataset-machine-translation bangla-machine-translation low-resource-languages emnlp-2020 low-resource-nlp low-resource-machine-translation | ai |
jakesmine | moved to http github com vic tintan | front_end |
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pikoRT | piko rt this is piko rt a tiny linux like real time operating system kernel optimized for arm cortex m series microprocessors run test suite run all test shell make plat stm32p103 check run all test with command line tools shell python m tests run specific test cases shell python m tests fs 1 cond 2 external source scripts rstlint py written by georg brandl | kernel rtos arm mcu cortex-m | os |
simple_tensorflow_serving | simple tensorflow serving images simple tensorflow serving introduction jpeg introduction simple tensorflow serving is the generic and easy to use serving service for machine learning models read more in https stfs readthedocs io x support distributed tensorflow models x support the general restful http apis x support inference with accelerated gpu x support curl and other command line tools x support clients in any programming language x support code gen client by models without coding x support inference with raw file for image models x support statistical metrics for verbose requests x support serving multiple models at the same time x support dynamic online and offline for model versions x support loading new custom op for tensorflow models x support secure authentication with configurable basic auth x support multiple models of tensorflow mxnet pytorch caffe2 cntk onnx h2o scikit learn xgboost pmml spark mllib installation install the server with pip https pypi python org pypi simple tensorflow serving bash pip install simple tensorflow serving or install from source code https github com tobegit3hub simple tensorflow serving bash python setup py install python setup py develop bazel build simple tensorflow serving server or use the docker image https hub docker com r tobegit3hub simple tensorflow serving bash docker run d p 8500 8500 tobegit3hub simple tensorflow serving docker run d p 8500 8500 tobegit3hub simple tensorflow serving latest gpu docker run d p 8500 8500 tobegit3hub simple tensorflow serving latest hdfs docker run d p 8500 8500 tobegit3hub simple tensorflow serving latest py34 bash docker compose up d or deploy in kubernetes https kubernetes io bash kubectl create f simple tensorflow serving yaml quick start start the server with the tensorflow savedmodel https www tensorflow org programmers guide saved model bash simple tensorflow serving model base path models tensorflow template application model check out the dashboard in http 127 0 0 1 8500 http 127 0 0 1 8500 in web browser dashboard images dashboard png generate python client and access the model with test data without coding bash curl http localhost 8500 v1 models default gen client language python client py bash python client py advanced usage multiple models it supports serve multiple models and multiple versions of these models you can run the server with this configuration json model config list name tensorflow template application model base path models tensorflow template application model platform tensorflow name deep image model base path models deep image model platform tensorflow name mxnet mlp model base path models mxnet mlp mx mlp platform mxnet bash simple tensorflow serving model config file examples model config file json adding or removing model versions will be detected automatically and re load latest files in memory you can easily choose the specified model and version for inference json endpoint http 127 0 0 1 8500 input data model name default model version 1 data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 result requests post endpoint json input data gpu acceleration if you want to use gpu try with the docker image with gpu tag and put cuda files in usr cuda files bash export cuda so v usr cuda files usr cuda files export devices ls dev nvidia xargs i echo device export library env e ld library path usr local cuda extras cupti lib64 usr local nvidia lib usr local nvidia lib64 usr cuda files docker run it p 8500 8500 cuda so devices library env tobegit3hub simple tensorflow serving latest gpu you can set session config and gpu options in command line parameter or the model config file bash simple tensorflow serving model base path models tensorflow template application model session config log device placement true allow soft placement true allow growth true per process gpu memory fraction 0 5 json model config list name default base path models tensorflow template application model platform tensorflow session config log device placement true allow soft placement true allow growth true per process gpu memory fraction 0 5 generated client you can generate the test json data for the online models bash curl http localhost 8500 v1 models default gen json or generate clients in different languages bash python golang javascript etc for your model without writing any code bash curl http localhost 8500 v1 models default gen client language python client py curl http localhost 8500 v1 models default gen client language bash client sh curl http localhost 8500 v1 models default gen client language golang client go curl http localhost 8500 v1 models default gen client language javascript client js the generated code should look like these which can be test immediately python usr bin env python import requests def main endpoint http 127 0 0 1 8500 json data model name default data keys 1 1 features 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 result requests post endpoint json json data print result text if name main main python usr bin env python import requests def main endpoint http 127 0 0 1 8500 input data keys 1 0 1 0 features 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 result requests post endpoint json input data print result text if name main main image model for image models we can request with the raw image files instead of constructing array data now start serving the image model like deep image model https github com tobegit3hub deep image model bash simple tensorflow serving model base path models deep image model then request with the raw image file which has the same shape of your model bash curl x post f image images mew jpg f model version 1 127 0 0 1 8500 tensorflow estimator model if we use the tensorflow estimator api to export the model the model signature should look like this inputs key inputs value name input example tensor 0 dtype dt string tensor shape dim size 1 outputs key classes value name linear binary logistic head classification output alternatives classes tensor 0 dtype dt string tensor shape dim size 1 dim size 1 outputs key scores value name linear binary logistic head predictions probabilities 0 dtype dt float tensor shape dim size 1 dim size 2 method name tensorflow serving classify we need to construct the string tensor for inference and use base64 to encode the string for http here is the example python code python def float feature value return tf train feature float list tf train floatlist value value def bytes feature value return tf train feature bytes list tf train byteslist value value def main raw input data feature dict a bytes feature 10 b float feature 10 create example as base64 string example proto tf train example features tf train features feature feature dict tensor proto tf contrib util make tensor proto example proto serializetostring dtype tf string tensor string tensor proto string val pop base64 tensor string base64 urlsafe b64encode tensor string request server endpoint http 127 0 0 1 8500 json data model name default base64 decode true data inputs base64 tensor string result requests post endpoint json json data print result json custom op if your models rely on new tensorflow custom op https www tensorflow org extend adding an op you can run the server while loading the so files bash simple tensorflow serving model base path model custom op paths foo op please check out the complete example in examples custom op examples custom op authentication for enterprises we can enable basic auth for all the apis and any anonymous request is denied now start the server with the configured username and password bash server py model base path models tensorflow template application model enable auth true auth username admin auth password admin if you are using the web dashboard just type your certification if you are using clients give the username and password within the request bash curl u admin admin h content type application json x post d data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 http 127 0 0 1 8500 python endpoint http 127 0 0 1 8500 input data data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 auth requests auth httpbasicauth admin admin result requests post endpoint json input data auth auth tsl ssl it supports tsl ssl and you can generate the self signed secret files for testing bash openssl req x509 newkey rsa 4096 nodes out tmp secret pem keyout tmp secret key days 365 then run the server with certification files bash simple tensorflow serving enable ssl true secret pem tmp secret pem secret key tmp secret key model base path models tensorflow template application model supported models for mxnet models you can load with commands and configuration like these bash simple tensorflow serving model base path models mxnet mlp mx mlp model platform mxnet python endpoint http 127 0 0 1 8500 input data model name default model version 1 data data 12 0 2 0 result requests post endpoint json input data print result text for onnx models you can load with commands and configuration like these bash simple tensorflow serving model base path models onnx mnist model onnx model proto model platform onnx python endpoint http 127 0 0 1 8500 input data model name default model version 1 data data result requests post endpoint json input data print result text for h2o models you can load with commands and configuration like these bash start h2o server with java jar h2o jar simple tensorflow serving model base path models h2o prostate model glm model python 1525255083960 17 model platform h2o python endpoint http 127 0 0 1 8500 input data model name default model version 1 data data result requests post endpoint json input data print result text for scikit learn models you can load with commands and configuration like these bash simple tensorflow serving model base path models scikitlearn iris model joblib model platform scikitlearn simple tensorflow serving model base path models scikitlearn iris model pkl model platform scikitlearn python endpoint http 127 0 0 1 8500 input data model name default model version 1 data data result requests post endpoint json input data print result text for xgboost models you can load with commands and configuration like these bash simple tensorflow serving model base path models xgboost iris model bst model platform xgboost simple tensorflow serving model base path models xgboost iris model joblib model platform xgboost simple tensorflow serving model base path models xgboost iris model pkl model platform xgboost python endpoint http 127 0 0 1 8500 input data model name default model version 1 data data result requests post endpoint json input data print result text for pmml models you can load with commands and configuration like these this relies on openscoring https github com openscoring openscoring and openscoring python https github com openscoring openscoring python to load the models bash java jar third party openscoring openscoring server executable 1 4 snapshot jar simple tensorflow serving model base path models pmml iris decisiontreeiris pmml model platform pmml python endpoint http 127 0 0 1 8500 input data model name default model version 1 data data result requests post endpoint json input data print result text supported client here is the example client in bash bash client bash curl h content type application json x post d data keys 1 0 2 0 features 10 10 10 8 6 1 8 9 1 6 2 1 1 1 1 7 1 1 http 127 0 0 1 8500 here is the example client in python python client python endpoint http 127 0 0 1 8500 payload data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 result requests post endpoint json payload here is the example client in c cpp client here is the example client in java java client here is the example client in scala scala client here is the example client in go go client go endpoint http 127 0 0 1 8500 databyte byte data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 var datainterface map string interface json unmarshal databyte datainterface datajson json marshal datainterface resp err http post endpoint application json bytes newbuffer datajson here is the example client in ruby ruby client ruby endpoint http 127 0 0 1 8500 uri uri parse endpoint header content type application json input data data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 http net http new uri host uri port request net http post new uri request uri header request body input data to json response http request request here is the example client in javascript javascript client javascript var options uri http 127 0 0 1 8500 method post json data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 request options function error response body here is the example client in php php client php endpoint 127 0 0 1 8500 inputdata array keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 jsondata array data inputdata ch curl init endpoint curl setopt array ch array curlopt post true curlopt returntransfer true curlopt httpheader array content type application json curlopt postfields json encode jsondata response curl exec ch here is the example client in erlang erlang client erlang ssl start application start inets httpc request post http 127 0 0 1 8500 application json data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 here is the example client in lua lua client lua local endpoint http 127 0 0 1 8500 keys array keys array 1 1 0 keys array 2 2 0 features array features array 1 1 1 1 1 1 1 1 1 1 features array 2 1 1 1 1 1 1 1 1 1 local input data keys keys array features features array local json data data input data request body json encode json data local response body local res code response headers http request url endpoint method post headers content type application json content length request body source ltn12 source string request body sink ltn12 sink table response body here is the example client in rust swift client here is the example client in swift swift client here is the example client in perl perl client perl my endpoint http 127 0 0 1 8500 my json data keys 11 0 2 0 features 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 my req http request new post endpoint req header content type application json req content json ua lwp useragent new response ua request req here is the example client in lisp swift client here is the example client in haskell swift client here is the example client in clojure clojure client here is the example client in r r client r endpoint http 127 0 0 1 8500 body list data list a 1 keys 1 json data list data list keys list list 1 0 list 2 0 features list list 1 1 1 1 1 1 1 1 1 list 1 1 1 1 1 1 1 1 1 r post endpoint body json data encode json stop for status r content r parsed text html here is the example with postman images postman png performance you can run simpletensorflowserving with any wsgi server for better performance we have benchmarked and compare with tensorflow serving find more details in benchmark benchmark stfs simple tensorflow serving and tfs tensorflow serving have similar performances for different models vertical coordinate is inference latency microsecond and the less is better images benchmark latency jpeg then we test with ab with concurrent clients in cpu and gpu tensorflow serving works better especially with gpus images benchmark concurrency jpeg for simplest model benchmark simplest model each request only costs 1 9 microseconds and one instance of simple tensorflow serving can achieve 5000 qps with larger batch size it can inference more than 1m instances per second images benchmark batch size jpeg how it works 1 simple tensorflow serving starts the http server with flask application 2 load the tensorflow models with tf saved model loader python api 3 construct the feed dict data from the json body of the request method post content type application json model version 1 optional data keys 1 2 features 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 4 use the tensorflow python api to sess run with feed dict data 5 for multiple versions supported it starts independent thread to load models 6 for generated clients it reads user s model and render code with jinja http jinja pocoo org templates images architecture jpeg contribution feel free to open an issue or send pull request for this project it is warmly welcome to add more clients in your languages to access tensorflow models | tensorflow-models savedmodel tensorflow serving client http machine-learning deep-learning | ai |
water-drinking-reminder | water drinking reminder the project made to remind to drink water this project for embedded systems design laboratory requirements esp wroom 32 bme280 spi for temperature humidity ds3231 i2c for real time clock ssd1306 i2c for oled buzzer module hw 040 for rotary encoder module 3 button module design alt text image design jpg schema alt text image schematic jpg setup 1 connect ssd1306 i2c to the esp32 br vcc 3v3 br gnd gnd br scl d22 scl br sda d21 sda br 2 connect ds3231 i2c to the esp32 br vcc 3v3 br gnd gnd br scl d22 scl br sda d21 sda br 3 connect bme280 spi to the esp32 br vcc 3v3 br gnd gnd br scl d18 br sda d23 mosi br csb d5 cs0 br sd0 d19 miso br 4 connect buzzer module to the esp32 br vcc 3v3 br gnd gnd br i o d13 gpio13 br 5 connect 3 button module to the esp32 br vcc 3v3 br gnd gnd br k1 d34 br k2 d35 br k3 d32 br 6 connect hw 040 rotary module to the esp32 br 3v3 br gnd gnd br sw d33 br dt d25 br clk d26 br installation 1 install library adafruit ssd1306 by adafruit for oled 2 install library adafruit gfx library by adafruit for oled 3 install library pubsubclient by nick o leary for mqtt client 4 install library arduinojson by benoit blanchon for using json serialization 5 install library tridenttd linenotify by tridenttd for using line notify 6 install library jc button by jack christensen for debouncing signals 7 install library rtclib by adafruit for setting current time 8 install library adafruit bme280 library by adafruit for using bme280 usage 1 setup thingspeak link guide https app tango us app workflow setup water habit tracker with thingspeak using mqtt d4d8b49085ea492ab4a4902507486bd5 2 setup line message api service link guide https app tango us app workflow line message api bot d784c38c28724f939111aaeadc92980f 3 setup line notify link guide https app tango us app workflow setup line notify 376edee9e87d41818e2ef3297e6651a5 troubleshooting get text from line message api service but cannot send it to esp32 i try both mqtt and thinghttp | os |
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fswd-lab-2 | full stack web development lab 2 hello world web server in node lab setup 1 clone the repository to your own machine be sure to run the git clone repository url command in the same directory as you ran the git clone command for lab 1 http betamore github io fswd lab 1 or at least outside the directory that contains your laptop s copy of the first lab 2 cd to the project directory and run npm install 3 run npm test to make sure everything is working you could also run npm t if the extra est is too much typing for you how does the web work when you enter a url into your browser or click on a link somewhere a whole series of things happen behind the scenes 1 the browser breaks up the url to be visited into different parts for example for the url http github com betamore fswd lab 2 protocol http host github com path betamore fswd lab 2 2 the browser then uses the domain name system of the internet dns for short to find the actual location to which to send the request you can do that step yourself by running host t a github com the result of that request is the internet protocol address ip address of the host 3 the browser determines where on the remote server to make the request based on the protocol the default location for the http protocol is port 80 for https it is 443 if the host part of the url is followed by some number the browser will use that as the port to which it connects 4 using the ip and port information the browser makes the connection to the web server this is the tcp part of tcp ip 5 once the connection is established the browser sends the actual web request to the server http get betamore fswd lab 2 http 1 1 accept host github com get is part of the http protocol it indicates to the web server what type of request is being made specifically in this case that it is a simple page information retrieval request other types of requests are post put delete patch head and more 6 after it receives the request the web server does what it needs to do and generates a response to send back to the browser http http 1 1 200 ok content type text html charset utf 8 date wed 13 sep 2017 17 58 41 gmt html some page stuff html the numeric value after the http 1 1 is the response status code it lets the browser know if the request succeeded or if not it will give the browser a clue as to what was wrong there are three primary ranges of status code values 200 299 the request was successful for various kinds of successful 300 399 the request was successful but the browser will need to make an additional request usually reflected in a location new url to request header in the response 400 499 there was a problem with the request itself ever heard of a 404 500 599 there was a problem handling the request on the server side or as i like to summarize it 200s everything is fine 300s go look over there 400s you the browser screwed up 500s i the web server screwed up 7 now that the browser has the response to its request it renders the html into the browser window or whatever happens to be the appropriate way to handle the response making inquiries we re going to use a tool to make web requests from the terminal httpie https httpie org you can install it with homebrew by running the command brew install httpie httpie uses another tool called curl internally to make the requests httpie simply makes using curl simplier and easier for all the httpie commands in this lab i will also provide the roughly equivalent curl commands to perform a get request for http google com sh http print hh google com curl vs d http google com o dev null 2 1 grep v w to perform a get request for http www google com sh http print hh www google com curl vs d http www google com o dev null 2 1 grep v w to perform a get request for https www google com sh http print hh https www google com curl vs d https www google com o dev null 2 1 grep v w running the application from the command line run npm run dev that will start a very simple node http server on your machine note that the program has to remain running to respond to any web requests to stop the server type control c hold down the control key and then press c your terminal will stop responding to commands while the application is running if you need to run any other commands you will need to open a new terminal window making requests to your server open chrome or your preferred browser and open the url http localhost 8000 http localhost 8000 what do you see in the browser dig deeper and open the web page source in your browser in chrome it s in view developer view source what do you see in your terminal where the server is running what happens when you request a different path on the server e g http localhost 8000 another page http localhost 8000 another page try making your requests in another terminal using the http or curl commands http verbose localhost 8000 or curl v http localhost 8000 adding new pages we have received a change request from product management now in addition to responding with hello world at the base url http localhost 8000 http localhost 8000 our application needs to respond to other urls in the pattern http localhost 8000 david http localhost 8000 david rarr hello david http localhost 8000 john http localhost 8000 john rarr hello john http localhost 8000 lee http localhost 8000 lee rarr hello lee to make this change update the function inside the http createserver call in lib server js there are already tests written for this new case in test server test js some useful javascript to keep in mind request in the function has all the information about the web request that you will need and request url specifically will contain the path being requested david john etc strings can be indexed just like arrays javascript var str abcs and 123s str 0 a str 1 b str 2 c str 3 s strings have a function like arrays have push and the others named replace for its basic usage it takes two arguments the string to replace and the string with which to replace it javascript var str abcs and 123s str replace abc xyz xyzs and 123s more information is available on the replace documentation on mdn https developer mozilla org en us docs web javascript reference global objects string replace | javascript | front_end |
word2vec | word2vec original from https code google com p word2vec i ve copied it to a github project so i can apply and track community patches for my needs starting with capability for mac os x compilation makefile and some source has been modified for mac os x compilation see https code google com p word2vec issues detail id 1 c5 memory patch for word2vec has been applied see https code google com p word2vec issues detail id 2 project file layout altered there seems to be a segfault in the compute accuracy utility to get started cd scripts demo word sh original readme text follows this tool provides an efficient implementation of the continuous bag of words and skip gram architectures for computing vector representations of words these representations can be subsequently used in many natural language processing applications and for further research tools for computing distributed representation of words we provide an implementation of the continuous bag of words cbow and the skip gram model sg as well as several demo scripts given a text corpus the word2vec tool learns a vector for every word in the vocabulary using the continuous bag of words or the skip gram neural network architectures the user should to specify the following desired vector dimensionality the size of the context window for either the skip gram or the continuous bag of words model training algorithm hierarchical softmax and or negative sampling threshold for downsampling the frequent words number of threads to use the format of the output word vector file text or binary usually the other hyper parameters such as the learning rate do not need to be tuned for different training sets the script demo word sh downloads a small 100mb text corpus from the web and trains a small word vector model after the training is finished the user can interactively explore the similarity of the words more information about the scripts is provided at https code google com p word2vec | ai |
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FileNetGuard | why filenetguard filenetguard is a security integrity verification software designed for linux servers and embedded systems it provides detection against unauthorized modifications to protect the integrity of your system and network features filenetguard offers the following key features regular security audits for files using advanced hashing algorithm sha 256 and network behavior verification algorithms detailed reports indicating any unauthorized modifications on files or ports from closed to open from unlistened to listened export the database to csv files for streamlined reporting and user friendly investigation report automation getting started to use filenetguard follow these simple steps install python3 if you don t already have it on archlinux bash sudo pacman syu sudo pacman s python3 python3 version on debian bash sudo apt update sudo apt install python3 python3 version clone this repository bash git clone https github com aetos arch filenetguard git cd filenetguard use basic commands bash usage filenetguard py h init report openport exportdb schedule periodic report port port debug options h help show this help message and exit init initialize report generate a report openport open a port exportdb export database in txt files schedule periodic report schedule periodic report port port specify the port to open debug enable debug mode filenetguard initializer bash python3 filenetguard py init generate a report bash python3 filenetguard py report export database in csv format bash python3 filenetguard py exportdb automate report generation bash python3 filenetguard py schedule periodic report contribution we welcome contributions from the open source community to enhance filenetguard s capabilities and security please read our contribution guidelines for more information license filenetguard is released under the apache license you are free to use modify and distribute this software | os |
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BulletGCSS | bullet gcss bullet gcss is a high caliber ground control station system designed for the 21st century lifestyle deploy bulletgcss ui on fpv sampa https github com danarrib bulletgcss workflows deploy 20bulletgcss 20ui 20on 20fpv 20sampa badge svg github social media https user images githubusercontent com 17026744 109589244 ca4d5880 7ae8 11eb 9c2f 4cdb2da3160a png bullet gcss allows an uav pilot operator to get the most important telemetry data right on his her smartphone or computer screen information such as aircraft location distance altitude battery status navigation status are always available the main differences between bullet gcss and other traditional ground station systems are it works using cellular data network which means that there s no maximum range you ll know about your aircraft as long as it s inside a the cellular network coverage area it doesn t require any app to be installed on your smartphone or computer it s just a web page that opens directly inside the web browser bullet gcss can also be installed on the smartphone as a web app giving the same experience but taking the full smartphone screen making it look even nicer it works both on android phones tablets and iphone ipad works on any pc too windows linux mac in fact it probably works on your windows phone too that s the beauty of web apps how it works there are two fundamental parts on bullet gcss the modem and the user interface ui modem talks to the flight controller on the aircraft to get the telemetry data and sends this data to a mqtt broker on the internet the ui is connected to this same mqtt broker and every time it gets a new telemetry message it ll display it on the screen how can i use it check out the wiki https github com danarrib bulletgcss wiki for detailed instructions | uav aircraft telemetry mqtt autopilot esp32 pwa | os |
system-design-interview | logo imgs systemcycle png https github com checkcheckzz system design interview how to prepare system design questions for an it company system design is a very broad topic even a software engineer with many years of working experience at a top it company may not be an expert on system design if you want to become an expert you need to read many books articles and solve real large scale system design problems this repository only teaches you how to handle the system design interview with a systematic approach in a short time you can dive into each topic if you have time of course welcome to add your thoughts a name toc table of contents a system design interview tips tips basic knowledge about system design intro company engineering blogs blog products and systems system hot questions and reference qs good books bk object oriented design ood toc a name tips system design interview tips a clarify the constraints and identify the user cases spend a few minutes questioning the interviewer and agreeing on the scope of the system remember to make sure you know all the requirements the interviewer didn t tell you about in the beginning user cases indicate the main functions of the system and constraints list the scale of the system such as requests per second requests types data written per second data read per second high level architecture design sketch the important components and the connections between them but don t go into some details usually a scalable system includes webserver load balancer service service partition database primary secondary database cluster plug cache component design for each component you need to write the specific apis for each component you may need to finish the detailed ood design for a particular function you may also need to design the database schema for the database toc a name intro basic knowledge about system design a here are some articles about system design related topics the anatomy of a system design interview https blog pramp com system design interview process e91aae2dbe83 how to succeed in a system design interview https blog pramp com how to succeed in a system design interview 27b35de0df26 how to rock a systems design interview http www palantir com 2011 10 how to rock a systems design interview system interview http www hiredintech com app system design scalability for dummies http www lecloud net tagged scalability scalable web architecture and distributed systems http www aosabook org en distsys html numbers everyone should know http everythingisdata wordpress com 2009 10 17 numbers everyone should know fallacies of distributed systems https pages cs wisc edu zuyu files fallacies pdf scalable system design patterns http horicky blogspot com 2010 10 scalable system design patterns html introduction to architecting systems for scale http lethain com introduction to architecting systems for scale transactions across datacenters http snarfed org transactions across datacenters io html a plain english introduction to cap theorem http ksat me a plain english introduction to cap theorem the cap faq https github com henryr cap faq paxos made simple http research microsoft com en us um people lamport pubs paxos simple pdf consistent hashing http www tom e white com 2007 11 consistent hashing html nosql patterns http horicky blogspot com 2009 11 nosql patterns html scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns of course if you want to dive into system related topics here is a good collection of reading list about services engineering https github com mmcgrana services engineering and a good collection of material about distributed systems http dancres github io pages toc a name blog company engineering blogs a if you are going to have an onsite with a company you should read their engineering blog high scalability http highscalability com the github blog https github com blog category engineering engineering at quora http engineering quora com yelp engineering blog http engineeringblog yelp com twitter engineering https engineering twitter com facebook engineering https www facebook com engineering yammer engineering http eng yammer com blog etsy code as craft http codeascraft com foursquare engineering blog http engineering foursquare com airbnb engineering https medium com airbnb engineering webengage engineering blog http engineering webengage com linkedin engineering http engineering linkedin com blog the netflix tech blog http techblog netflix com banksimple simple blog https www simple com engineering square the corner http corner squareup com soundcloud backstage blog https developers soundcloud com blog flickr code http code flickr net instagram engineering http instagram engineering tumblr com dropbox tech blog https tech dropbox com cloudera developer blog http blog cloudera com bandcamp tech http bandcamptech wordpress com oyster tech blog http tech oyster com the reddit blog http www redditblog com groupon engineering blog https engineering groupon com songkick technology blog http devblog songkick com google ai blog https ai googleblog com google developers blog https developers googleblog com pinterest engineering blog http engineering pinterest com twilio engineering blog http www twilio com engineering bitly engineering blog http word bitly com uber engineering blog https eng uber com godaddy engineering http engineering godaddy com splunk blog http blogs splunk com coursera engineering blog https building coursera org paypal engineering blog https www paypal engineering com nextdoor engineering blog https engblog nextdoor com booking com development blog https blog booking com microsoft engineering blog https engineering microsoft com scalyr engineering blog https blog scalyr com myntra engineering blog https medium com myntra engineering fastly blog https www fastly com blog aws architecture blog https aws amazon com blogs architecture lyft engineering blog https eng lyft com wish engineering https medium com wish engineering doordash engineering https doordash engineering snowflake blog https community snowflake com s blog palantir blog https medium com palantir tech home toc a name system products and systems a the following papers articles slides can help you to understand the general design idea of different real products and systems mapreduce simplified data processing on large clusters http static googleusercontent com media research google com zh cn us archive mapreduce osdi04 pdf bigtable a distributed storage system for structured data http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf the google file system http static googleusercontent com media research google com zh cn us archive gfs sosp2003 pdf the chubby lock service for loosely coupled distributed systems http static googleusercontent com external content untrusted dlcp research google com en us archive chubby osdi06 pdf dynamo amazon s highly available key value store http www read seas harvard edu kohler class cs239 w08 decandia07dynamo pdf introduction to memcached http www slideshare net oemebamo introduction to memcached cassandra introduction features http www slideshare net planetcassandra cassandra introduction features 30103666 introduction to hbase http www slideshare net alexbaranau intro to hbase introduction to mongodb http www slideshare net mdirolf introduction to mongodb introduction to redis http www slideshare net dvirsky introduction to redis storm http www slideshare net previa storm 16094009 introduction to zookeeper http www slideshare net sauravhaloi introduction to apache zookeeper kafka http www slideshare net mumrah kafka talk tri hug youtube architecture http highscalability com youtube architecture scaling pinterest http highscalability com blog 2013 4 15 scaling pinterest from 0 to 10s of billions of page views a html google architecture http highscalability com google architecture scaling twitter http highscalability com scaling twitter making twitter 10000 percent faster the whatsapp architecture http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html flickr architecture http highscalability com flickr architecture amazon architecture http highscalability com amazon architecture stack overflow architecture http highscalability com blog 2009 8 5 stack overflow architecture html pinterest architecture http highscalability com blog 2012 5 21 pinterest architecture update 18 million visitors 10x growth html tumblr architecture http highscalability com blog 2012 2 13 tumblr architecture 15 billion page views a month and harder html instagram architecture http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html tripadvisor architecture http highscalability com blog 2011 6 27 tripadvisor architecture 40m visitors 200m dynamic page view html scaling mailbox http highscalability com blog 2013 6 18 scaling mailbox from 0 to one million users in 6 weeks and 1 html salesforce architecture http highscalability com blog 2013 9 23 salesforce architecture how they handle 13 billion transacti html espn architecture http highscalability com blog 2013 11 4 espns architecture at scale operating at 100000 duh nuh nuhs html uber architecture http highscalability com blog 2015 9 14 how uber scales their real time market platform html dropbox design https www youtube com watch v pe4gwstwhmc splunk architecture http www splunk com view sp caaabf9 toc a name qs hot questions and reference a there are some good references for each question the references here are slides and articles design a cdn network reference globally distributed content delivery https kilthub cmu edu articles journal contribution globally distributed content delivery 6605972 design a google document system reference google mobwrite https code google com p google mobwrite differential synchronization https neil fraser name writing sync design a random id generation system reference announcing snowflake https blog twitter com 2010 announcing snowflake snowflake https github com twitter snowflake design a key value database reference introduction to redis http www slideshare net dvirsky introduction to redis design the facebook news feed function reference what are best practices for building something like a news feed http www quora com what are best practices for building something like a news feed what are the scaling issues to keep in mind while developing a social network feed http www quora com activity streams what are the scaling issues to keep in mind while developing a social network feed activity feeds architecture http www slideshare net danmckinley etsy activity feeds architecture design the facebook timeline function reference building timeline https www facebook com note php note id 10150468255628920 facebook timeline http highscalability com blog 2012 1 23 facebook timeline brought to you by the power of denormaliza html design a function to return the top k requests during past time interval reference efficient computation of frequent and top k elements in data streams http www cse ust hk raywong comp5331 references efficientcomputationoffrequentandtop kelementsindatastreams pdf an optimal strategy for monitoring top k queries in streaming windows http davis wpi edu xmdv docs edbt11 diyang pdf design an online multiplayer card game reference how to create an asynchronous multiplayer game http www indieflashblog com how to create an asynchronous multiplayer game html how to create an asynchronous multiplayer game part 2 saving the game state to online database http www indieflashblog com how to create async part2 html how to create an asynchronous multiplayer game part 3 loading games from the database http www indieflashblog com how to create async part3 html how to create an asynchronous multiplayer game part 4 matchmaking http www indieflashblog com how to create async part4 html html comment 4447 real time multiplayer in html5 http buildnewgames com real time multiplayer design a graph search function reference building out the infrastructure for graph search https www facebook com notes facebook engineering under the hood building out the infrastructure for graph search 10151347573598920 indexing and ranking in graph search https www facebook com notes facebook engineering under the hood indexing and ranking in graph search 10151361720763920 the natural language interface of graph search https www facebook com notes facebook engineering under the hood the natural language interface of graph search 10151432733048920 and erlang at facebook http www erlang factory com upload presentations 31 eugeneletuchy erlangatfacebook pdf design a picture sharing system reference flickr architecture http highscalability com flickr architecture instagram architecture http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html design a search engine reference how would you implement google search http programmers stackexchange com questions 38324 interview question how would you implement google search implementing search engines http www ardendertat com 2012 01 11 implementing search engines design a recommendation system reference hulu s recommendation system http tech hulu com blog 2011 09 19 recommendation system html recommender systems http ijcai13 org files tutorial slides td3 pdf design a tinyurl system reference system design for big data tinyurl http n00tc0d3r blogspot com url shortener api https developers google com url shortener csw 1 design a garbage collection system reference baby s first garbage collector http journal stuffwithstuff com 2013 12 08 babys first garbage collector design a scalable web crawling system reference how can i build a web crawler from scratch https www quora com how can i build a web crawler from scratch design the facebook chat function reference erlang at facebook http www erlang factory com upload presentations 31 eugeneletuchy erlangatfacebook pdf facebook chat https www facebook com note php note id 14218138919 id 9445547199 index 0 design a trending topic system reference implementing real time trending topics with a distributed rolling count algorithm in storm http www michael noll com blog 2013 01 18 implementing real time trending topics in storm early detection of twitter trends explained http snikolov wordpress com 2012 11 14 early detection of twitter trends design a cache system reference introduction to memcached http www slideshare net oemebamo introduction to memcached toc a name bk good books a big data principles and best practices of scalable realtime data systems http www amazon com big data principles practices scalable dp 1617290343 real time analytics techniques to analyze and visualize streaming data http www amazon com real time analytics techniques visualize streaming dp 1118837916 building microservices designing fine grained systems http www amazon com building microservices sam newman dp 1491950358 designing data intensive applications the big ideas behind reliable scalable and maintainable systems https www amazon com designing data intensive applications reliable maintainable dp 1449373321 toc a name ood object oriented design a tips for ood interview clarify the scenario write out user cases use case is a description of sequences of events that taken together lead to a system doing something useful who is going to use it and how they are going to use it the system may be very simple or very complicated special system requirements such as multi threading read or write oriented define objects map identity to class one scenario for one class each core object in this scenario for one class consider the relationships among classes certain class must have unique instance one object has many other objects composition one object is another object inheritance identify attributes for each class change noun to variable and action to methods use design patterns such that it can be reused in multiple applications useful websites 101 design patterns tips for developers http sourcemaking com design patterns and tips | interview interview-questions interview-preparation design-systems system system-design | os |
litbank | litbank litbank is an annotated dataset of 100 works of english language fiction to support tasks in natural language processing and the computational humanities described in more detail in the following publications david bamman sejal popat and sheng shen 2019 an annotated dataset of literary entities http people ischool berkeley edu dbamman pubs pdf naacl2019 literary entities pdf naacl 2019 matthew sims jong ho park and david bamman 2019 literary event detection http people ischool berkeley edu dbamman pubs pdf acl2019 literary events pdf acl 2019 david bamman olivia lewke and anya mansoor 2020 an annotated dataset of coreference in english literature https arxiv org abs 1912 01140 lrec litbank currently contains annotations for entities events entity coreference and quotation attribution in a sample of 2 000 words from each of those texts totaling 210 532 tokens a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by 4 0 88x31 png a br span xmlns dct http purl org dc terms href http purl org dc dcmitype dataset property dct title rel dct type litbank span is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a entity annotations the entity annotation layer of litbank covers six of the ace 2005 categories in text people per tom sawyer her daughter facilities fac the house the kitchen geo political entities gpe london the village locations loc the forest the river vehicles veh the ship the car organizations org the army the church the targets of annotation here include both named entities e g tom sawyer and common entities the boy these entities can be nested as in the following img src img nested structure png alt drawing width 300 for more see david bamman sejal popat and sheng shen an annotated dataset of literary entities http people ischool berkeley edu dbamman pubs pdf naacl2019 literary entities pdf naacl 2019 event annotations the event layer in litbank identifies events with asserted realis depicted as actually taking place with specific participants at a specific time as opposed to events with other epistemic modalities hypotheticals future events extradiegetic summaries by the narrator text events source my father s eyes had closed upon the light of this world six months when mine opened on it closed opened dickens david copperfield call me ishmael melville moby dick his sister was a tall strong girl and she walked rapidly and resolutely as if she knew exactly where she was going and what she was going to do next walked cather o pioneers for more see matt sims jong ho park and david bamman literary event detection http people ischool berkeley edu dbamman pubs pdf acl2019 literary events pdf acl 2019 coreference annotations the coreference layer in litbank covers the six ace entity categories outlined above people facilities locations geo political entities organizations and vehicles while the entity tagging above only covers proper noun phrases tom sawyer and common noun phrases the boy the coref annotations also cover personal pronouns he as well we annotate three different categories of coreference phenomena coreference of identity which links a mention in text to a discourse entity copula which links an attribute mention to another mention and apposition which links an appositive expression to another mention phenomenon example source coreference one may as well begin with helen sub x sub s letters to her sub x sub sister sub y sub forster howard s end copula img src img copula png alt drawing width 200 melville bartleby the scrivener apposition img src img apposition png alt drawing width 350 conrad heart of darkness annotations largely follow ontonotes guidelines though singleton mentions are annotated here with several departures for literary style including the distinction between generic specific mentions near identity and the revelation of identity for more on the coreference criteria used in these annotations see david bamman olivia lewke and anya mansoor 2020 an annotated dataset of coreference in english literature https arxiv org abs 1912 01140 lrec quotation annotations the quotation layer in litbank identifies all instances of direct speech in the text attributed to its speaker quote speaker source come up kinch come up you fearful jesuit buck mulligan 0 joyce ulysses oh dear oh dear i shall be late the white rabbit 4 carroll alice in wonderland do n t put your feet up there huckleberry miss watson 26 twain huckleberry finn this layer captures dialogue in quotation marks and other typographical markers such as dashes in joyce s ulysses and excludes strings wrapped in quotation marks that do not constitute dialogue such as the use of scare quotes for emphatic use for jargon neologisms or irony titles of works of art and the mention of a term as distinct from its use speaker labels are identical to those in the coref section of litbank to enable linking the quotation and coreference layers for more on the quotation annotations see this paper https arxiv org pdf 2004 13980 pdf tagger a trained tagger which can be used to tag entities and events in new text can be found in the tagger directory a model for coreference resolution is coming shortly corpus the corpus is drawn from the public domain texts on project gutenberg and includes individual works of fiction both novels and short stories that include a mix of high literary style e g edith wharton s age of innocence james joyce s ulysses and popular pulp fiction e g h rider haggard s king solomon s mines horatio alger s ragged dick we select approximately 2 000 words from each of 100 texts the total annotated dataset contains 210 532 tokens gutenberg id date author title 514 1868 alcott louisa may little women 18581 1904 alger horatio jr adrift in new york tom and florence braving the world 5348 1868 alger horatio jr ragged dick or street life in new york with the boot blacks 158 1815 austen jane emma 105 1818 austen jane persuasion 1342 1813 austen jane pride and prejudice 1206 1914 bower b m the flying u ranch 969 1848 bront anne the tenant of wildfell hall 1260 1847 bront charlotte jane eyre an autobiography 768 1847 bront emily wuthering heights 2095 1853 brown william wells clotelle a tale of the southern states 113 1911 burnett frances hodgson the secret garden 6053 1778 burney fanny evelina or the history of a young lady s entrance into the world 62 1912 burroughs edgar rice a princess of mars 78 1912 burroughs edgar rice tarzan of the apes 2084 1903 butler samuel the way of all flesh 11 1865 carroll lewis alice s adventures in wonderland 24 1913 cather willa o pioneers 44 1915 cather willa the song of the lark 472 1900 chesnutt charles w charles waddell the house behind the cedars 1695 1908 chesterton g k gilbert keith the man who was thursday a nightmare 160 1899 chopin kate the awakening and selected short stories 1155 1922 christie agatha the secret adversary 155 1868 collins wilkie the moonstone 219 1899 conrad joseph heart of darkness 974 1907 conrad joseph the secret agent a simple tale 940 1826 cooper james fenimore the last of the mohicans a narrative of 1757 73 1895 crane stephen the red badge of courage an episode of the american civil war 876 1861 davis rebecca harding life in the iron mills or the korl woman 521 1719 defoe daniel the life and adventures of robinson crusoe 1023 1852 dickens charles bleak house 766 1849 dickens charles david copperfield 1400 1861 dickens charles great expectations 730 1838 dickens charles oliver twist 1661 1892 doyle arthur conan the adventures of sherlock holmes 2852 1902 doyle arthur conan the hound of the baskervilles 233 1900 dreiser theodore sister carrie a novel 15265 1911 du bois w e b william edward burghardt the quest of the silver fleece a novel 145 1871 eliot george middlemarch 550 1861 eliot george silas marner 12677 1914 ferber edna personality plus some experiences of emma mcchesney and her son jock 6593 1749 fielding henry history of tom jones a foundling 9830 1922 fitzgerald f scott francis scott the beautiful and damned 805 1920 fitzgerald f scott francis scott this side of paradise 2775 1915 ford ford madox the good soldier 2641 1908 forster e m edward morgan a room with a view 2891 1910 forster e m edward morgan howards end 4276 1855 gaskell elizabeth cleghorn north and south 32 1915 gilman charlotte perkins herland 502 1913 grey zane desert gold 3457 1919 grey zane the man of the forest 711 1887 haggard h rider henry rider allan quatermain 2166 1885 haggard h rider henry rider king solomon s mines 27 1874 hardy thomas far from the madding crowd 110 1891 hardy thomas tess of the d urbervilles a pure woman 77 1851 hawthorne nathaniel the house of the seven gables 33 1850 hawthorne nathaniel the scarlet letter 95 1894 hope anthony the prisoner of zenda 41 1820 irving washington the legend of sleepy hollow 208 1879 james henry daisy miller a study 432 1903 james henry the ambassadors 209 1898 james henry the turn of the screw 367 1896 jewett sarah orne the country of the pointed firs 2807 1899 johnston mary to have and to hold 4217 1916 joyce james a portrait of the artist as a young man 2814 1914 joyce james dubliners 4300 1922 joyce james ulysses 217 1913 lawrence d h david herbert sons and lovers 543 1920 lewis sinclair main street 215 1903 london jack the call of the wild 351 1915 maugham w somerset william somerset of human bondage 11231 1853 melville herman bartleby the scrivener a story of wall street 2489 1851 melville herman moby dick or the whale 45 1908 montgomery l m lucy maud anne of green gables 41286 1866 oliphant mrs margaret miss marjoribanks 60 1905 orczy emmuska orczy baroness the scarlet pimpernel 932 1839 poe edgar allan the fall of the house of usher 1064 1842 poe edgar allan the masque of the red death 4051 1915 praed campbell mrs lady bridget in the never never land a story of australian life 3268 1794 radcliffe ann ward the mysteries of udolpho 434 1908 rinehart mary roberts the circular staircase 171 1791 rowson mrs charlotte temple 271 1877 sewell anna black beauty 84 1823 shelley mary wollstonecraft frankenstein or the modern prometheus 120 1883 stevenson robert louis treasure island 345 1897 stoker bram dracula 829 1726 swift jonathan gulliver s travels into several remote nations of the world 8867 1918 tarkington booth the magnificent ambersons 599 1848 thackeray william makepeace vanity fair 76 1884 twain mark adventures of huckleberry finn 74 1876 twain mark the adventures of tom sawyer 1327 1898 von arnim elizabeth elizabeth and her german garden 238 1915 webster jean dear enemy 5230 1897 wells h g herbert george the invisible man a grotesque romance 36 1897 wells h g herbert george the war of the worlds 541 1920 wharton edith the age of innocence 174 1890 wilde oscar the picture of dorian gray 2005 1919 wodehouse p g pelham grenville piccadilly jim 16357 1788 wollstonecraft mary mary a fiction 1245 1919 woolf virginia night and day format the entity data is formatted in the original brat http brat nlplab org standoff annotation format and in the tab separated layered format of https github com meizhiju layered bilstm crf https github com meizhiju layered bilstm crf commons we welcome contributions to litbank in the form of annotations for new texts in the public domain and suggestions for new texts to include please contact dbamman berkeley edu to get involved for more information see interannotator com http www interannotator com public litbank entities 155 the moonstone brat to explore the current entity annotations in litbank through the brat annotation interface try annotating entities in o pioneers http www interannotator com private index xhtml commons test 24 o pioneers brat yourself login by hovering over the brat icon in the upper right corner using username test and password annotate acknowledgments this work was supported by an amazon research award natural language processing for literary texts | ai |
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frontend-nanodegree-resume | project details how do i complete this project review the online resume project rubric https review udacity com ga 1 189245867 12280332 1465333852 projects 2962818615 rubric 1 in this project you will store your resume data in four javascript objects according to the schema given below as is often the case when leveraging an api the objects must follow the schema exactly all properties must be present and have real or fake values the names must match those in the schema note that object and property names are case sensitive all property values should be of the data type given for the property in the schema for example if the data type is given as an array it is not acceptable to use a string as a value for that property 2 once you ve created your javascript objects you will write the code needed to display all of the resume data contained within these objects in your resume 3 all of the html code needed to build the resume is stored in js helper js variables the variable names indicate their function you will replace substrings in these variable string values such as data and with the data in your javascript objects and append or prepend the formatted result to your resume in the appropriate location 4 if you need a refresher on javascript syntax go to the javascript basics https classroom udacity com nanodegrees nd001 parts 0011345406 modules 296281861575460 lessons 1946788554 concepts 25505685350923 course if you would like to understand how this project is manipulating and traversing the dom check out intro to jquery https classroom udacity com nanodegrees nd001 parts 0011345406 modules 296281861575461 lessons 3314378535 concepts 33166386820923 5 go through the videos and assignments in this course to learn the javascript necessary to build your resume 6 fork the project repo from github https github com udacity frontend nanodegree resume and begin building you resume 7 if you are prompted to do so you may want to get a google maps api key https developers google com maps documentation javascript get api key and include it as the value of the key parameter when loading the google maps api in index html script src http maps googleapis com maps api js libraries places key your api key script you may have some initial concerns with placing your api key directly within your javascript source files but rest assured this is perfectly safe all client side code must be downloaded by the client therefore the client must download this api key it is not intended to be secret google has security measures in place to ensure your key is not abused it is not technically possible to make anything secret on the client side 8 check your work against the project rubric https review udacity com ga 1 189245867 12280332 1465333852 projects 2962818615 rubric 9 when you are satisfied with your project submit it according to the submission instructions below by the end your resume will look something like this http i imgur com pwu1xbl png and your repository will include the following files index html the main html document contains links to all of the css and js resources needed to render the resume including resumebuilder js js helper js contains helper code needed to format the resume and build the map it also has a few function shells for additional functionality more on helper js further down js resumebuilder js this file is empty you should write your code here js jquery js the jquery library css style css contains all of the css needed to style the page readme md the github readme file and some images in the images directory your starting point js helper js within helper js you ll find a large collection of strings containing snippets of html within many snippets you ll find placeholder data in the form of data or contact each string has a title that describes how it should be used for instance htmlworkstart should be the first div in the work section of the resume htmlschoollocation contains a data placeholder which should be replaced with the location of one of your schools your process the resume has four distinct sections work education projects and a header with biographical information you ll need to 1 build four javascript objects each one representing a different resume section the objects that you create including property names and the data types of their values need to follow the schema below exactly all properties should be included and contain a value of the type specified unless the property is marked optional property values may contain real or fake data property names are case sensitive make sure your javascript objects are formatted correctly using jshint com http jshint com bio contains name string role string contacts an object with mobile string email string github string twitter string optional location string welcomemessage string skills array of strings biopic string url display function taking no parameters education contains schools array of objects with name string location string degree string majors array of strings dates string works with a hyphen between them url string optional onlinecourses array of objects with title string school string dates string works with a hyphen between them url string display function taking no parameters work contains jobs array of objects with employer string title string location string dates string can be in progress description string display function taking no parameters projects contains projects array of objects with title string dates string works with a hyphen between them description string images array with string urls display function taking no parameters 2 iterate through each javascript object and append its information to index html in the correct section first off you ll be using jquery s selector append and selector prepend functions to modify index html selector append makes an element appear at the end of a selected section selector prepend makes an element appear at the beginning of a selected section pay close attention to the ids of the div s in index html and the html snippets in helper js they ll be very useful as jquery selectors for selector append and selector prepend you ll also be using the javascript method string replace old new to swap out all the placeholder text e g data for data from your resume json objects here s an example of some code that would add the location of one of your companies to the page var formattedlocation htmlworklocation replace data work jobs job location work entry last append formattedlocation use the mockup at the page of this document as a guide for the order in which you should append elements to the page 3 the resume includes an interactive map do the following to add it in resumebuilder js append the googlemap string to div id mapdiv in index html uncomment the google script element script type text javascript src http maps googleapis com maps api js libraries places script in helper js at the bottom of the file uncomment code to initialize map and set fitbounds 4 all of your code for adding elements to the resume should be contained within functions 5 as described in the javascript object schema each display function should be encapsulated within the javascript object it displays in the resume for instance your display function for appending work experience data to the resume should be encapsulated within the work javascript object the display function can be encapsulated within the work javascript object definition in the same way other properties are defined there or it can be encapsulated later in the file using dot notation for example work display 6 it s possible to make additional information show up when you click on the pins in the map check out line 174 in helper js and the google maps api to get started archival note this repository is deprecated therefore we are going to archive it however learners will be able to fork it to their personal github account but cannot submit prs to this repository if you have any issues or suggestions to make feel free to utilize the https knowledge udacity com forum to seek help on content specific issues submit a support ticket along with the link to your forked repository if learners are blocked for other reasons here are the links for the retail consumers https udacity zendesk com hc en us requests new and enterprise learners https udacityenterprise zendesk com hc en us requests new ticket form id 360000279131 | front_end |
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STM32F429_CubeMX_LVGL_FreeRTOS | stm32f429 stm32cube lvgl f429 stm32cube esp32 f429 stm32cubemx hal usart freertos fatfs sdio cube hal usart rtc sdio fatfs sdram ltdc dma2d freertos touchgfx littlevgl c canvas stm32cubemx hal csdn https blog csdn net sxf1061700625 usart 1 bug hal bug hal uart receive it hal uart transmit it hal lock huart printf 2 hal uart receive it cr1 rxneie 0 hal uart receive it hal lock huart freertos fatfs sdio freertos sd freertos mout read 3 not ready sdio sddma freertos stm32cubemx https sxf1024 lanzoui com b09rf2dwj bgvi https sxf1024 lanzoui com b09rf2dwj cube hal cube 1 2 3 pinout configuration rcc hse crystal ceramic resonator sys debug serial wiire 4 clock configuration image 20201012091155867 https gitee com songxf1024 markdownpicbed raw master typora img image 20201012091155867 png 5 project manager https gitee com songxf1024 markdownpicbed raw master typora img e6 97 a0 e6 a0 87 e9 a2 98 png 6 pinout configuration gpio pinout view categories parameter 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uint8 t second tmp 0 hal rtc gettime hrtc stime rtc format bin hal rtc getdate hrtc sdate rtc format bin if second tmp stime seconds second tmp stime seconds printf 20 d d d d d d r n sdate year 10 10 sdate year 10 sdate month 10 10 sdate month 10 sdate date 10 10 sdate date 10 printf d d d d d d r n stime hours 10 10 stime hours 10 stime minutes 10 10 stime minutes 10 stime seconds 10 10 stime seconds 10 sdio fatfs 1 sdio pinout clock configuration connectivity sdio mode sd 4bit wide bus nvic image 20201012093115874 https gitee com songxf1024 markdownpicbed raw master typora img image 20201012093115874 png 2 sdio clock configuration sdio 48mhz yes sdio clkdiv sdio ck 48mhz clkdiv 2 24mhz img src https gitee com songxf1024 markdownpicbed raw master typora img image 20201012093007042 png alt image 20201012093007042 style zoom 80 3 pinout clock configuration middleware fatfs sd code page simplified chinese use len image 20201012093606060 https gitee com songxf1024 markdownpicbed raw 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retsd fr ok printf mount error d r n retsd retsd f open sdfile 0 test txt fa create always fa write if retsd fr ok printf open error d r n retsd retsd f write sdfile abcde 5 bw if retsd fr ok printf write error d r n retsd f close sdfile retsd f open sdfile 0 test txt fa read char buff 10 0 retsd f read sdfile buff 5 bw if retsd fr ok printf s r n buff f close sdfile sdram 1 fmc pinout configuration connectivity fmc sdram2 sdram1 0xc0000000 sdram2 0xd0000000 sdram 4 bank configuration sdram image 20201014121059733 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014121059733 png clock and chip enable fmc sdcke0 fmc sdclk0 1 0xc000 0000 0xcfff ffff fmc sdcke1 fmc sdclk1 2 0xd000 0000 0xdfff ffff bank sdram bank 2 column bit number 8 row bit number 12 cas latency cas cl sdram 2 memory clock cycles sdram 133mhz 3 memory clock cycles write protection disabled sdram common clock sdram hclk 2 3 sdclk 216mhz sdram common clock 2 sdclk 108mhz 9 25ns sdram common burst read sdram common read pipe delay cas 0 hclk clock cycle load mode register to active delay img https gitee com songxf1024 markdownpicbed raw master typora img 141338jvprpqplpi64lqj8 png exit self refresh delay 70ns 9 25ns 8 70 9 25 img https gitee com songxf1024 markdownpicbed raw master typora img 141338o1ocj10qicgqdjsg png sdram common row cycle delay 63ns 9 25ns 7 63 9 25 img https gitee com songxf1024 markdownpicbed raw master typora img 141338m7g7k9u8tyljv66r png write recovery time img https gitee com songxf1024 markdownpicbed raw master typora img 141338xvouu4u33mk4vl0a png sdram common row precharge delay 15ns 9 25ns 2 15 9 25 img https gitee com songxf1024 markdownpicbed raw master typora img 141338c5yokzquobourcrk png row to column delay 15ns 9 25ns 2 15 9 25 twr tras trcd twr trc trcd trp twr write recovery time 2 tras self refresh time 5 trc sdram common row cycle delay 7 trp sdram common row precharge delay 2 trcd row to column delay row to column delay 3 img https gitee com songxf1024 markdownpicbed raw master typora img 141339pa631kk6zbz0wh6m png 2 generate code keil c uint8 t temp 100 attribute at 0xd0000000 for int i 0 i 100 i temp i i for int i 0 i 100 i printf d temp i 3 cube sdram c users 10617 stm32cube repository stm32cube fw f4 v1 25 0 drivers bsp stm32f429i discovery xxx c sdram brief sdram param none retval none static void user sdram enable void fmc sdram commandtypedef command io uint32 t tmpmrd 0 step 1 configure a clock configuration enable command command commandmode fmc sdram cmd clk enable command commandtarget fmc sdram cmd target bank2 command autorefreshnumber 1 command moderegisterdefinition 0 send the command hal sdram sendcommand hsdram1 command sdram timeout step 2 insert 100 us minimum delay inserted delay is equal to 1 ms due to systick time base unit ms hal delay 1 step 3 configure a pall precharge all command command commandmode fmc sdram cmd pall command commandtarget fmc sdram cmd target bank2 command autorefreshnumber 1 command moderegisterdefinition 0 send the command hal sdram sendcommand hsdram1 command sdram timeout step 4 configure an auto refresh command command commandmode fmc sdram cmd autorefresh mode command commandtarget fmc sdram cmd target bank2 command autorefreshnumber 4 command moderegisterdefinition 0 send the command hal sdram sendcommand hsdram1 command sdram timeout step 5 program the external memory mode register tmpmrd uint32 t sdram modereg burst length 2 sdram modereg burst type sequential sdram modereg cas latency 3 sdram modereg operating mode standard sdram modereg writeburst mode single command commandmode fmc sdram cmd load mode command commandtarget fmc sdram cmd target bank2 command autorefreshnumber 1 command moderegisterdefinition tmpmrd send the command hal sdram sendcommand hsdram1 command sdram timeout step 6 set the refresh rate counter set the device refresh rate hal sdram programrefreshrate hsdram1 refresh count stm32f429igt6 sdram 8m https sxf1024 lanzoui com b09rf535a https sxf1024 lanzoui com b09rf535a bf5q core keil sdram initsequence hal sdram mspinit fmc c include bsp sdram h mx fmc init sdram initsequence sdram test ltdc dma2d sdram https zzttzz gitee io blog posts 7109b92c https sxf1024 lanzoui com b09rf535a https sxf1024 lanzoui com b09rf535a bf5q pixel clock plparity normal input dma2d lcd tft 25mhz 1 layer 0 2 layer 1 img https gitee com songxf1024 markdownpicbed raw master typora img 20170821102811043 img src https gitee com songxf1024 markdownpicbed raw master typora img 20170821102936357 alt img style zoom 67 freertos touchgfx freertos fatfs sdio stm32cubemx freertos 1 freertos image 20201014165023823 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014165023823 png 1 https gitee com songxf1024 markdownpicbed raw master typora img 20191129141331476 png 2 freertos systick rtos hal systick hal sys pinout tim6 image 20201014165408107 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014165408107 png rtos freertos library max syscall interrupt priority 3 freertos dma fatfs dma sdio fatfs sdio 5 freertos 5 image 20201014203641765 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014203641765 png 4 mout sd clkdiv image 20201014203506378 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014203506378 png 5 project manager project linker setting sd image 20201014203746258 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014203746258 png 6 freertos minimal stack size 128 f mount 256 image 20201014203856605 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014203856605 png 7 keil rtos defaulttask freertos c 8 sd task sd defaulttask image 20201014204217089 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014204217089 png 9 ltdc task image 20201014204526297 https gitee com songxf1024 markdownpicbed raw master typora img image 20201014204526297 png 10 11 osthreadnew xtaskcreate 12 tim6 touchgfx c 1 cube touchgfx image 20201015090331929 https gitee com songxf1024 markdownpicbed raw master typora img image 20201015090331929 png image 20201015090406926 https gitee com songxf1024 markdownpicbed raw master typora img image 20201015090406926 png 2 freertos cmsis v1 image 20201015090537967 https gitee com songxf1024 markdownpicbed raw master typora img image 20201015090537967 png 3 touchgfx image 20201015090522472 https gitee com songxf1024 markdownpicbed raw master typora img image 20201015090522472 png 3 keil 4 touchgfx designer c users 10617 stm32cube repository packs stmicroelectronics x cube touchgfx 4 15 0 utilities pc software touchgfxdesigner touchgfx 4 14 0 msi 5 cube touchgfx touchgfx img https gitee com songxf1024 markdownpicbed raw master typora img 20190723101707319 png https gitee com songxf1024 markdownpicbed raw master typora img 36668c8a6cc4cd0aae4872f3ee2617af png https gitee com songxf1024 markdownpicbed raw master typora img 928daaa0211ca13528e4d391ffd51c2e png 6 keil c include app touchgfx h lcd lcd displayon lcd setlayervisible 1 disable lcd setlayervisible 0 enable lcd settransparency 1 0 lcd settransparency 0 255 lcd selectlayer 0 touchgfx mx touchgfx process 7 microlib c keil image 20201015084256974 https gitee com songxf1024 markdownpicbed raw master typora img image 20201015084256974 png 8 printf microlib c if 1 pragma import use no semihosting struct file int handle file stdout sys exit void sys exit int x x x void ttywrch int ch ch ch fputc int fputc int ch file f hal uart transmit huart1 uint8 t ch 1 0xff return ch endif 9 littlevgl c littltvgl c c emwin window xp littlevgl android github https github com lvgl lvgl https github com lvgl lvgl https docs lvgl io latest en html https docs lvgl io latest en html lvgl 3 5 quick overview 15 10 lvgl 2 3 lvgl 15 1 csdn https blog csdn net t01051 article details 108748462 https www waveshare net study article 964 1 html no space https blog csdn net qq 36075612 article details 107671669 csdn https blog csdn net zcy cyril article details 107457371 sram sdram https blog csdn net qq 41543888 article details 106532577 2 cube mtm dma dma2d image 20201015143713645 https gitee com songxf1024 markdownpicbed raw master typora img image 20201015143713645 png 3 disp flush c static variables static io uint16 t my fb io uint16 t 0xd0000000 static dma handletypedef dmahandle static int32 t x1 flush static int32 t y1 flush static int32 t x2 flush static int32 t y2 fill static int32 t y fill act static const lv color t buf to flush static lv disp t our disp null includes include lv port disp h include bsp lcd h include dma2d h include stm32f4xx hal dma h include dma h static void disp flush lv disp drv t disp drv const lv area t area lv color t color p int32 t x int32 t y for y area y1 y area y2 y for x area x1 x area x2 x put a pixel to the display for example put px x y color p lcd fillrect c area x1 area y1 area x2 area x1 area y2 area y1 uint32 t color p lcd drawpixel x y uint32 t color p full lcd fillrect c x y 1 1 uint32 t color p full color p int32 t x1 area x1 int32 t x2 area x2 int32 t y1 area y1 int32 t y2 area y2 return if the area is out the screen if x2 0 return if y2 0 return if x1 lv hor res max 1 return if y1 lv ver res max 1 return truncate the area to the screen int32 t act x1 x1 0 0 x1 int32 t act y1 y1 0 0 y1 int32 t act x2 x2 lv hor res max 1 lv hor res max 1 x2 int32 t act y2 y2 lv ver res max 1 lv ver res max 1 y2 x1 flush act x1 y1 flush act y1 x2 flush act x2 y2 fill act y2 y fill act act y1 buf to flush color hal statustypedef err uint32 t length x2 flush x1 flush 1 if lv color depth 24 lv color depth 32 length 2 stm32 dma uses 16 bit chunks so multiply by 2 for 32 bit color endif err hal dma start it hdma memtomem dma2 stream0 uint32 t buf to flush uint32 t my fb y fill act lv hor res max x1 flush length if err hal ok printf disp flush d r n err while 1 halt on error lv disp flush ready disp drv c int32 t x1 area x1 int32 t x2 area x2 int32 t y1 area y1 int32 t y2 area y2 return if the area is out the screen if x2 0 return if y2 0 return if x1 lv hor res max 1 return if y1 lv ver res max 1 return truncate the area to the screen int32 t act x1 x1 0 0 x1 int32 t act y1 y1 0 0 y1 int32 t act x2 x2 lv hor res max 1 lv hor res max 1 x2 int32 t act y2 y2 lv ver res max 1 lv ver res max 1 y2 for int32 t y act y1 y act y2 y for int32 t x act x1 x act x2 x put a pixel to the display for example put px x y color p my fb y lv hor res max x uint32 t color p full color p 4 littlvgl sdram 5 tim6 100ms tim7 1 5ms c hal tim base start it htim7 6 keil c lv init lv port disp init lcd lcd displayon lcd setlayervisible 1 disable lcd setlayervisible 0 enable lcd settransparency 1 0 lcd settransparency 0 255 lcd selectlayer 0 lcd clear lcd color blue create a label on the currently active screen lv obj t label1 lv label create lv scr act null modify the label s text lv label set text label1 hello world align the label to the center null means align on parent which is the screen now 0 0 at the end means an x y offset after alignment lv obj align label1 null lv align center 0 0 include bsp lcd h static void disp flush lv disp drv t disp drv const lv area t area lv color t color p int32 t x int32 t y for y area y1 y area y2 y for x area x1 x area x2 x lcd drawpixel x y uint32 t color p full color p lv disp flush ready disp drv https sxf1024 lanzoui com b09rf535a https sxf1024 lanzoui com b09rf535a bf5q lv init lv port disp init ui display buff screen win part screen lv obj t screen1 lv obj create null null window lv obj t win lv win create screen1 null label lv obj t label1 lv label create win null label text lv label set text label1 hello world lv obj align label1 null lv align center 0 0 screen lv scr load screen1 style c static lv style t loading style lv obj t loading label lv label create lv scr act null lv label set text loading label loading lv obj align loading label null lv align center 0 0 lv style init loading style lv style set text color loading style lv state default lv color blue lv style set text font loading style lv state default lv font montserrat 24 lv obj add style loading label lv obj part main loading style c c https littlevgl com image to c array img https gitee com songxf1024 markdownpicbed raw master typora img 112926lqax24r2s926xzu9 png c extern const lv img t my image name lv img declare my image name c const lv img dsc t waveshare logo header always zero 0 header w 287 header h 81 data size 11728 header cf lv img cf indexed 4bit data waveshare logo map c lv img declare waveshare logo lv obj t img1 lv img create lv scr act null lv img set src img1 waveshare logo lv obj align img1 null lv align center 0 20 canvas c buff static lv color t buffer lv canvas buf size true color 48 48 canvas lv obj t canvas lv canvas create lv scr act null canvas buff lv canvas set buffer canvas buffer 48 48 lv img cf true color lv canvas fill bg canvas lv color blue lv opa 50 lv color t c0 c0 full 0 uint32 t x uint32 t y for y 10 y 30 y for x 5 x 20 x x y lv canvas set px canvas x y c0 1 https github com lvgl lv fs if 2 c h 3 lv conf h c file system interface define lv use fs if 1 if lv use fs if define lv fs if fatfs 0 s define lv fs if pc 0 endif lv use fs if 4 0 s fatfs sd 5 lv fs if init 6 lv fs fatfs c 7 open close read seek tell 8 c sd retsd f mount sdfatfs sdpath 0 lv fs if init lv obj t icon lv img create lv scr act null sd lv img set src icon s 0 bin lv obj align icon null lv align center 0 0 http www lfly xyz forum php mod viewthread tid 21 lvglfonttool v0 4 http www lfly xyz forum php mod viewthread tid 11 extra page 3d1 keil utf8 img src https gitee com songxf1024 markdownpicbed raw master typora img image 20201026133629769 png alt image 20201026133629769 style zoom 80 image 20201026133655619 https gitee com songxf1024 markdownpicbed raw master typora img image 20201026133655619 png lvgl switchtogbk utf8 unicode user sxf image 20201026220214722 https gitee com songxf1024 markdownpicbed raw master typora img image 20201026220214722 png myfont c sd c static uint8 t user font getdata int offset int size spi flash spiflash read g font buf offset size sdram return uint8 t sdram fontddr offset uint32 t br if f open sdfile const tchar 0 myfont bin fa read fr ok printf myfont bin open failed r n else if f lseek sdfile fsize t offset fr ok printf myfont bin lseek failed r n if f read sdfile g font buf uint size uint br fr ok printf myfont bin lseek failed r n printf offset d t size d t g font buf s r n offset size g font buf f close sdfile return g font buf c static lv style t date style lv style init date style lv obj t date label2 lv label create lv scr act null lv label set text date label2 123y lv style set text color date style lv state default lv color red lv style set text font date style lv state default myfont lv obj add style date label2 lv obj part main date style lv obj align date label2 null lv align in top left 10 10 lv obj set pos date label2 10 10 | os |
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W601_IoT_Board | rt thread w60x sdk rt thread w60x sdk w601 iot board w60x sdk rt thread w601 iot board w601 iot board w601 iot board docs figures hardware w60x hw origin png ieee802 11n 1t1r cortex m3 80mhz 1mb flash 288kbyte sram rf transceiver cmos pa sdio spi uart gpio i c pwm i s 7816 prng sha1 md5 rc4 des 3des aes crc rsa w60x sdk w60x sdk mdk iar gcc 01 led blink led 02 key 03 rgb led rgb led 04 beep 05 ir 06 lcd lcd 07 temp humi 08 als ps 09 fs tf card tf 10 fal flash fal flash 11 kv easyflash kv 12 fs flash spi flash 13 ulog 14 adbd adb 15 micropython micropython 16 wifi manager wifi manager wifi 17 web config wifi web wifi 18 airkiss airkiss wifi 19 atk module atk 20 at server at 21 mqtt paho mqtt mqtt 22 http client web client http client 23 web server web webnet 24 websocket websocket 25 cjson json 26 tls mbedtls tls 27 hw crypto 28 ota ymodem ymodem ota 29 ota http http ota 30 netutils 31 cloud rtt rt thread 32 cloud onenet onenet 33 cloud ali iotkit 34 cloud ms azure azure 35 cloud tencent 36 demo wm librarie wm librarie wifi wm librarie app wifi wifi w60x uart spi timer pwm wdg wifi wifi sta softap ap sta wifi wifi wifi sdk sdk docs board an0001 rt thread pdf an0002 rt thread gpio pdf gpio an0003 rt thread i2c pdf i2c an0004 rt thread spi pdf spi an0006 rt thread qemu pdf qemu rt thread an0009 rt thread systemview pdf systemview an0010 rt thread pdf lwip an0011 rt thread pdf an0012 rt thread pdf rt thread an0014 rt thread at pdf rt thread at an0017 rt thread pdf rt thread an0018 rt thread pdf rt thread netutils an0020 rt thread eclipse qemu pdf eclipse qemu rt thread an0021 rt thread vs code qemu pdf vs code qemu rt thread an0022 rt thread ulog pdf rt thread ulog an0023 rt thread qemu pdf qemu rt thread an0024 rt thread ulog pdf rt thread ulog an0025 rt thread pdf rt thread pdf rt thread um1001 rt thread webclient pdf webclient um1002 rt thread ali iotkit pdf rt thread ali iotkit um1003 rt thread onenet pdf rt thread onenet um1004 rt thread ota pdf rt thread ota um1005 rt thread paho mqtt pdf paho mqtt um1006 rt thread mbedtls pdf rt thread mbedtls um1007 rt thread azure iot sdk pdf azure iot sdk um1008 rt thread pdf rt thread um1009 rt thread pdf power management um1010 rt thread web webnet pdf rt thread webnet um3101 rt thread w60x sdk pdf rt thread w60x sdk um3102 rt thread w60x sdk pdf rt thread w60x sdk um3103 rt thread w60x sdk pdf rt thread w60x sdk um3104 rt thread w60x sdk pdf rt thread w60x sdk wm librarie wm librarie sdk rt thread w60x sdk libraries wm libraries doc oneshot lib demo app w60x qflash driver for swd keil jtag qflash wm w600 oneshotconfig2 0 android sdk v1 0 pdf android sdk wm w600 oneshotconfig2 0 ios sdk v1 0 pdf ios sdk wm w600 rom v1 1 pdf rom wm w600 secboot v1 0 pdf secboot wm w600 swd v1 2 pdf swd wm w600 v1 1 pdf wm w600 v1 1 pdf wm w600 v1 1 pdf wm w601 v1 2 pdf w60x sdk rt thread rt thread rt thread rt thread https www rt thread org document site | server |
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FreeRTPS-FreeRTOS | freertps freertos integration between freertos and freertps documentation is available at http wiki ros org freertps 2bfreertos this package uses freertps freertps is a free portable minimalist rtps implementation documentation is available at freertps wiki https github com ros2 freertps wiki page | os |
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freeRTOS-PIC24-dsPIC-PIC32MM | introduction this repository contains the freertos demos for microchip device families like pic24 dspic33e dspic33f dspic33c and pic32mm for the demo applications mplab x and mplab xc16 are the preferred ide and compiler respectively with which to build the freertos demos the board to be used to run the demo is explorer 16 32 explore 16 32 is backward compatible with explorer 16 the version of freertos used in this demo is freertos v9 directories the freertos source directory contains the freertos source code and contains its own readme file see http www freertos org a00017 html for full details of the directory structure and information on locating the files you require see http www freertos org portpic24 dspic html demoapp for full details above the demos the easiest way to use freertos is to start with one of the pre configured demo application projects found in the freertos demo directory that way you will have the correct freertos source files included and the correct include paths configured once a demo application is building and executing you can remove the demo application file and start to add in your own application source files see also http www freertos org freertos quick start guide html http www freertos org faqhelp html | os |
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responsively-app | div align center img src https responsively app assets img logo png alt responsively logo width 150 h1 responsively app a href https github com responsively org responsively app releases latest target blank img alt github release src https img shields io github v release responsively org responsively app a h1 strong a must have devtool for web developers for quicker responsive web development strong h6 save time by becoming 5x faster h6 div br p align center a href https twitter com responsivelyapp target blank img src https img shields io twitter follow responsivelyapp color 26a0ed label follow logo twitter logocolor white style flat alt twitter a a href contributors target blank img src https img shields io github all contributors responsively org responsively app style flat alt contributors a a href https responsively app join discord target blank img src https img shields io badge join 20 discord blue logo discord logocolor white alt discord a a href https xscode 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souce badge responsively target blank img src https api producthunt com widgets embed image v1 top post badge svg post id 200375 theme light period daily alt producthunt a p p align center download now free a href https responsively app download target blank responsively app a p br responsively app a modified browser built using electron https www electronjs org that helps in responsive web development br quick demo https responsively app assets img responsively app gif features 1 mirrored user interactions across all devices 2 customizable preview layout to suit all your needs 3 one handy elements inspector for all devices in preview 4 30 built in device profiles with option to add custom devices 5 one click screenshot all your devices 6 hot reloading supported for developers please visit the website to know more about the application https responsively app download the application is available for mac windows and linux platforms please a href https responsively app download target blank download it from responsively app a alternatively macos users can use brew https formulae brew sh cask responsively a href https formulae brew sh cask responsively target blank img src https badgen net homebrew cask dy responsively alt homebrew installs a bash brew install cask responsively also windows users can use chocolatey https chocolatey org packages responsively a href https chocolatey org packages responsively target blank img src https img shields io chocolatey dt responsively alt chocolatey installs a bash choco install responsively or winget https github com microsoft winget cli bash winget install responsivelyapp linux users using an rpm package manager can use rpm bash sudo rpm i https github com responsively org responsively app releases download v version responsively app version x86 64 rpm otherwise download an appimage from the releases page https github com responsively org responsively app releases follow us on twitter for future updates twitter follow https 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become a sponsor and have your company logo here https opencollective com responsively contribute to get started with contributing your awesome ideas to responsively follow the comprehensive guide here https github com responsively org responsively app blob main contributing md get in touch come say hi to us on discord https responsively app join discord wave contributors thanks go to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tbody tr td align center valign top width 20 a href http twitter com vivek jonam img src https avatars1 githubusercontent com u 1283424 v 4 s 100 width 100px alt manoj vivek br sub b manoj vivek b sub a br a href https github com responsively org responsively app commits author manojvivek title code a a href https github com responsively org responsively app commits author manojvivek title tests a a href projectmanagement manojvivek title project management a td td align center valign top width 20 a href https github com esprush img src https avatars0 githubusercontent com u 26249498 v 4 s 100 width 100px alt suresh p br sub b suresh p b sub a br a href https github com responsively org responsively app commits author esprush title code a a href https github com responsively org responsively app commits author esprush title tests a a href projectmanagement esprush title project management a td td align center valign top width 20 a href https github com sprabowo img src https avatars2 githubusercontent com u 11748183 v 4 s 100 width 100px alt sigit prabowo br sub b sigit prabowo b sub a br a href https github com responsively org responsively app commits author sprabowo title code a td td align center valign top width 20 a href https github com leon0707 img src https avatars1 githubusercontent com u 523684 v 4 s 100 width 100px alt leon feng br sub b leon feng b sub a br a href https github com responsively org responsively app commits author leon0707 title documentation a td td align center valign top width 20 a href https github com kishoreio img src https avatars2 githubusercontent com u 30261988 v 4 s 100 width 100px alt kishore s br sub b kishore s b sub a br a href https github com responsively org responsively app commits author kishoreio title code a td tr tr td align center valign top width 20 a href https jjavierdguezas github io img src https avatars2 githubusercontent com u 13673443 v 4 s 100 width 100px alt jos javier rodr guez zas br sub b jos javier rodr guez zas b sub a br a href https github com responsively org responsively app commits author jjavierdguezas title code a a href https github com responsively org responsively app commits author jjavierdguezas title tests a td td align center valign top width 20 a href https github com romanakash img src https avatars1 githubusercontent com u 40427975 v 4 s 100 width 100px alt roman akash br sub b 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b sub a br a href https github com responsively org responsively app commits author afermon title code a td td align center valign top width 20 a href https github com danial gharib img src https avatars githubusercontent com u 90343552 v 4 s 100 width 100px alt danial gharib br sub b danial gharib b sub a br a href https github com responsively org responsively app commits author danial gharib title code a td td align center valign top width 20 a href http www twitter com s3lf img src https avatars githubusercontent com u 1087128 v 4 s 100 width 100px alt alexander menk br sub b alexander menk b sub a br a href https github com responsively org responsively app commits author amenk title documentation a td td align center valign top width 20 a href http tunahangediz com img src https avatars githubusercontent com u 75015671 v 4 s 100 width 100px alt tunahan gediz br sub b tunahan gediz b sub a br a href https github com responsively org responsively app commits author tunahangediz 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src https avatars githubusercontent com u 49458912 v 4 s 100 width 100px alt steven cabrera br sub b steven cabrera b sub a br a href https github com responsively org responsively app commits author lyncasterc title code a td td align center valign top width 20 a href https github com negar 75 img src https avatars githubusercontent com u 113235504 v 4 s 100 width 100px alt negar nasiri br sub b negar nasiri b sub a br a href https github com responsively org responsively app commits author negar 75 title code a td tr tr td align center valign top width 20 a href https github com gauravsingh94 img src https avatars githubusercontent com u 99260988 v 4 s 100 width 100px alt gaurav singh br sub b gaurav singh b sub a br a href https github com responsively org responsively app commits author gauravsingh94 title code a td td align center valign top width 20 a href https github com nishidhjain img src https avatars githubusercontent com u 61869195 v 4 s 100 width 100px alt nishidh jain br sub b nishidh jain b sub a br a href https github com responsively org responsively app commits author nishidhjain title code a td td align center valign top width 20 a href https github com mishrasamiksha img src https avatars githubusercontent com u 38784342 v 4 s 100 width 100px alt samiksha mishra br sub b samiksha mishra b sub a br a href https github com responsively org responsively app commits author mishrasamiksha title documentation a td tr tbody table markdownlint restore prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome | web-development responsive responsive-web-design desktop-app electron developer-tools good-first-issue contributions-welcome react redux javascript opensource-alternative hacktoberfest | front_end |
data_engineering_project | data engineering project de project the data engineering project has as main goal to demonstrate how to properly design and develop two things etl project rest api this project consists of designing a database to load data based on a sample file shared by our client its creation initial data load and usage as part of a crud api are part of the considerations of such design another relevant component of the project is the development of the different pieces considered relevant during the design process requirements the project can be built with docker compose the project is based on both the docker compose and sample data files provided by our clients all the services can be executed and tested by using docker compose the initial data load happens by pushing the data from the cent os machine built with docker the api supports the following actions read mechanism to fetch data from the database must insert mechanism optional update mechanism optional delete mechanism optional the server where the api is going to be deployed must have access only to the postgres database and the centos server must have access only to the postgres database as well assumptions 1 every single one of our contacts work for a single company at a time 2 our contacts may have more than one address where we may reach them 3 our contacts at least have one phone number but may have more than one 4 our contacts at least have one email address but may have more than one 5 our contacts may be working in more than one department at a time reason why we have more than one record for some of our contacts 6 primary keys used when ingesting data into the postgresql database are the first name and last name 7 in case an entity is found twice in the csv the following logics is going to be applied 7 1 if the department is different proceed to insert it as a new relationship with our contact 7 2 if the address is different proceed to insert it as a new relationship with our contact no record has that condition in the sample csv but adding the assumption since it seems like a nice opportunity to make it more interesting 8 no combination of first name and last name refers to a different person entity relationship diagram database proposed design documentation img data engineering project png project structure data this directory stores the files with the data for the initial data loading process documentation this folder stores the files that explain technically the project and its components sql init this path stores the sql file s that creates the database overall structure for our data engineering project installation usage final thoughts | server |
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nlp-notebooks | nlp notebooks a collection of notebooks for natural language processing from nlp town nlp 101 1 an introduction to word embeddings https github com nlptown nlp notebooks blob master an 20introduction 20to 20word 20embeddings ipynb 2 nlp with pre trained models from spacy and stanfordnlp https github com nlptown nlp notebooks blob master nlp 20with 20pretrained 20models 20 20spacy 20and 20stanfordnlp ipynb 3 discovering and visualizing topics in texts with lda https github com nlptown nlp notebooks blob master discovering 20and 20visualizing 20topics 20in 20texts 20with 20lda ipynb named entity recognition 1 updating spacy s named entity recognition system https github com nlptown nlp notebooks blob master updating 20spacy s 20named 20entity 20recognition 20system ipynb 2 named entity recognition with conditional random fields https github com nlptown nlp notebooks blob master named 20entity 20recognition 20with 20conditional 20random 20fields ipynb 3 sequence labelling with a bilstm in pytorch https github com nlptown nlp notebooks blob master sequence 20labelling 20with 20a 20bilstm 20in 20pytorch ipynb 4 medical entity recognition with pretrained transformers https github com nlptown nlp notebooks blob master medical 20entity 20recognition 20with 20pretrained 20transformers ipynb text classification 1 traditional text classification with scikit learn https github com nlptown nlp notebooks blob master traditional 20text 20classification 20with 20scikit learn ipynb 2 intent classification with smaller transformers https github com nlptown nlp notebooks blob master intent 20classification 20with 20small 20transformers ipynb 3 zero shot text classification https github com nlptown nlp notebooks blob master zero shot 20text 20classification ipynb sentence similarity 1 simple sentence similarity https github com nlptown nlp notebooks blob master simple 20sentence 20similarity ipynb 2 data exploration with sentence similarity data 20exploration 20with 20sentence 20similarity ipynb multilingual word embeddings 1 introduction https github com nlptown nlp notebooks blob master multilingual 20embeddings 20 201 20introduction ipynb 2 cross lingual sentence similarity https github com nlptown nlp notebooks blob master multilingual 20embeddings 20 202 20cross lingual 20sentence 20similarity ipynb 3 cross lingual transfer learning https github com nlptown nlp notebooks blob master multilingual 20embeddings 20 203 20transfer 20learning ipynb transfer learning 1 keras sentiment analysis with elmo embeddings https github com nlptown nlp notebooks blob master elmo 20embeddings ipynb 2 text classification with bert in pytorch https github com nlptown nlp notebooks blob master text 20classification 20with 20bert 20in 20pytorch ipynb 3 multilingual text classification with bert https github com nlptown nlp notebooks blob master multilingual 20text 20classification 20with 20bert ipynb | natural-language-processing text-mining deep-learning artificial-intelligence nlp word-embeddings | ai |
multipwm | multipwm multipwm library for esp open rtos example c include espressif esp common h include esp uart h include freertos h include task h include multipwm h void multipwm task void pvparameters uint32 t counts 500 2100 3000 int32 t steps 100 300 200 uint8 t pins 12 13 15 pwm info t pwm info pwm info channels 3 multipwm init pwm info for uint8 t ii 0 ii pwm info channels ii multipwm set pin pwm info ii pins ii while 1 multipwm stop pwm info for uint8 t ii 0 ii pwm info channels ii multipwm set duty pwm info ii counts ii counts ii steps ii if counts ii 10000 counts ii 0 steps ii steps ii multipwm start pwm info vtaskdelay 1 void user init void uart set baud 0 115200 printf sdk version s n sdk system get sdk version xtaskcreate multipwm task multipwm 256 null 2 null | esp8266 pwm esp-open-rtos freertos multipwm multi-pwm softpwm | os |
AliceVision | alicevision photogrammetric computer vision framework https github com alicevision alicevision raw develop docs logo alicevision banner png alicevision http alicevision github io is a photogrammetric computer vision framework which provides a 3d reconstruction and camera tracking algorithms alicevision aims to provide strong software basis with state of the art computer vision algorithms that can be tested analyzed and reused the project is a result of collaboration between academia and industry to provide cutting edge algorithms with the robustness and the quality required for production usage learn more details about the pipeline and tools based on it on alicevision website http alicevision github io see results of the pipeline on sketchfab http sketchfab com alicevision photogrammetry photogrammetry is the science of making measurements from photographs it infers the geometry of a scene from a set of unordered photographies or videos photography is the projection of a 3d scene onto a 2d plane losing depth information the goal of photogrammetry is to reverse this process see the presentation of the pipeline steps http alicevision github io photogrammetry license the project is released under mplv2 see copying md copying md citation if you use this project for a publication please cite the paper https hal archives ouvertes fr hal 03351139 inproceedings alicevision2021 title a licevision m eshroom an open source 3d reconstruction pipeline author carsten griwodz and simone gasparini and lilian calvet and pierre gurdjos and fabien castan and benoit maujean and gregoire de lillo and yann lanthony booktitle proceedings of the 12th acm multimedia systems conference mmsys 21 doi 10 1145 3458305 3478443 publisher acm press year 2021 bibliography see bibliography bibliography md for the list of research papers and tools used in this project get the project get the source code git clone recursive git github com alicevision alicevision see install md install md to build the project continuous integration status build status https travis ci org alicevision alicevision png branch develop https travis ci org alicevision alicevision coverage status https coveralls io repos github alicevision alicevision badge png branch develop https coveralls io github alicevision alicevision branch develop launch 3d reconstructions use meshroom https github com alicevision meshroom to launch the alicevision pipeline meshroom provides a user interface to create 3d reconstructions meshroom provides a command line to launch all the steps of the pipeline meshroom is written in python and can be used to create your own python scripts to customize the pipeline or create custom automation the user interface of meshroom relies on qt and pyside the meshroom engine and command line has no dependency to qt contact use the public mailing list to ask questions or request features it is also a good place for informal discussions like sharing results interesting related technologies or publications alicevision googlegroups com mailto alicevision googlegroups com http groups google com group alicevision http groups google com group alicevision you can also contact the core team privately on alicevision team googlegroups com mailto alicevision team googlegroups com contributing cii best practices https bestpractices coreinfrastructure org projects 2995 badge https bestpractices coreinfrastructure org projects 2995 beyond open source interest to foster developments open source is a way of life the project has started as a collaborative project and aims to continue we love to exchange ideas improve ourselves while making improvements for other people and discover new collaboration opportunities to expand everybody s horizon contributions are welcome we integrate all contributions as soon as it is useful for someone don t create troubles for others and the code quality is good enough for maintainance please have a look at the project code of conduct code of conduct md to provide a friendly motivating and welcoming environment for all please have a look at the project contributing guide contributing md to provide an efficient workflow that minimize waste of time for contributors and maintainers as well as maximizing the project quality and efficiency use github pull requests to submit contributions http github com alicevision alicevision issues http github com alicevision alicevision issues use the public mailing list to ask questions or request features and use github issues to report bugs http github com alicevision alicevision pulls http github com alicevision alicevision pulls project history in 2009 cmp research team from ctu started the phd thesis of michal jancosek supervised by tomas pajdla they released windows binaries of their mvs pipeline called cmpmvs in 2012 in 2009 toulouse inp inria and duran duboi started a french anr project to create a model based camera tracking solution based on natural features and a new marker design called cctag in 2010 mikros image and imagine research team a joint research group between ecole des ponts paristech and centre scientifique et technique du batiment started a partnership around pierre moulon s thesis supervised by renaud marlet and pascal monasse on the academic side and benoit maujean on the industrial side in 2013 they released an open source sfm pipeline called openmvg multiple view geometry to provide the basis of a better solution for the creation of visual effects matte paintings in 2015 simula toulouse inp and mikros image joined their efforts in the eu project popart to create a previz system based on alicevision in 2017 ctu join the team in the eu project ladio to create a central hub with structured access to all data generated on set based on alicevision see contributors md contributors md for the full list of contributors we hope to see you in this list soon | computer-vision 3d-reconstruction photogrammetry structure-from-motion camera-tracking multiview-stereo panorama-image panorama-stitching hdri-image alicevision meshroom | ai |
wiz | wiz ide wiz is ide for web development using angular more easy screenshot https github com season framework wiz blob main screenshot wiz gif installation install nodejs npm angular bash apt install nodejs npm npm i g n n stable install wiz python package bash pip install season install pip install season upgrade upgrade lastest usage create project and start web server bash cd workspace wiz create myapp cd myapp wiz run port 3000 http 127 0 0 1 3000 wiz on your web browser start server as daemon bash wiz server start start daemon server wiz server stop stop daemon server regist system service for linux bash run on wiz project root directory wiz service regist myapp wiz service start myapp upgrade ide from command line bash pip install season upgrade upgrade core wiz ide upgrade ide upgrade wiz cli create project wiz create project name example bash wiz create myapp build project wiz build project project name clean flag flag syntax description project wiz build project name build project default main clean wiz build clean clean build default false example bash wiz build project main wiz build project dev clean daemon api wiz run host host port port log log file path flag flag syntax description port wiz run action port port web server port default 3000 host wiz run action host host web server host default 0 0 0 0 log wiz run action log path log file path default none example bash wiz run port 3000 wiz run port 3000 host 0 0 0 0 wiz run port 3000 log wiz log wiz server action log log file path force action action syntax description start wiz server start flags start wiz server as daemon stop wiz server stop flags stop wiz server daemon restart wiz server restart flags restart wiz server daemon flag flag syntax description log wiz server action log path log file path default none force wiz server start force force start daemon example bash wiz server start force wiz server stop wiz server restart service api wiz service list example bash wiz service list wiz service regist name port same as install example bash wiz service regist myapp or wiz service install myapp src 3001 or wiz service install myapp bundle 3001 wiz service unregist name same as uninstall remove delete rm example bash wiz service unregist myapp or wiz service remove myapp wiz service status name example bash wiz service status myapp wiz service start name example bash wiz service start myapp wiz service start start all services wiz service stop name example bash wiz service stop myapp wiz service stop stop all services wiz service restart name example bash wiz service restart myapp wiz service restart restart all services bundle project wiz pkg project project name example bash wiz pkg bundle main project wiz pkg project main output workspace bundle file created after run bundle api run using command wiz run bundle or adding services using wiz service install myservice bundle version policy x y z x major update upgrade not supported y minor update support command upgrade core function changed required server restart z ui update support upgrade from web ui not required server restart release note 2 3 24 core command change bundle pkg 2 3 21 2 3 23 core change requirement for python old version support 2 3 20 plugin workspace create widget bug at portal module fixed 2 3 19 core add dependency flask socketio 2 3 18 core upgrade to angular 16 core color changed core add build command plugin workspace tree view component changed plugin git commit bug fixed 2 3 17 core wiz response stream api 2 3 16 plugin workspace bug at app create fixed 2 3 15 core cache added for wiz config 2 3 14 core cache added for wiz components model controller api 2 3 13 core bundle command added 2 3 12 core service command upgraded add bundle option 2 3 11 core service command upgraded add port option 2 3 10 core boot config changed 2 3 9 core boot config changed 2 3 8 plugin workspace portal framework widget create bug fixed 2 3 7 plugin workspace statusbar bug fixed 2 3 6 plugin workspace npm plugin bug fixed core default plugin config bug fixed portal framework 2 3 5 core assets path bug fixed 2 3 4 core bundle path bug fixed 2 3 3 plugin workspace config list bug fixed 2 3 2 plugin workspace app json bug fixed 2 3 1 plugin workspace portal framework controller bug fixed 2 3 0 core move build logic to ide plugin core add bundle structure core localize angular cli core add linux service cli core add statusbar at bottom of ide plugin define model at plugin plugin workspace angular build logic changed plugin workspace integrated portal framework plugin at workspace plugin workspace build portal framework on builder model 2 2 x major issues ide overlay menu shortcut config plugin user customized plugin portal add portal framework plugin plugin workspace refresh list bug fixed core ide monaco editor bug fixed plugin workspace usability improvements plugin core auto complete keyword core toastr on build error plugin workspace hidden portal framework on route plugin workspace image viewer core angular version upgrade core typescript dependencies bug fixed 2 1 x major issues ide plugin concept changed ide layout changed ide config concept added plugin core move to app link in monaco editor plugin core add core plugins upgrade button plugin core add restart server button plugin workspace add app route editor service plugin workspace preview bug fixed plugin workspace page namespace bug fixed plugin workspace set default code if component ts not exists plugin workspace import create app bug fixed plugin core remove useless log plugin workspace config folder bug fixed plugin bug fixed remove unused file plugin workspace add route build plugin workspace remove useless log plugin core plugin updated core add lib plugin object command bug fixed 2 0 x major issues upgrade base project to angular 14 2 0 ui ux full changed drag and drop interface git branch to project multiple project in workspace enhanced ide plugin and easily develop 3rd party apps support pip and npm on ui ide socket auto install angular cli angular 15 flask response bug fixed on filesend wiz bundle mode update wiz server command multiprocess config bug fixed socketio bug fixed ide controller threading bug fixed flask socketio 1 0 x major issue clean code full changed ide remove season flask concept enhanced performance logging for wiz concept upgrade plugin structure config structure changed stable version for git merge add wiz server start log file method print bug fixed add daemon server command socket io transport server starting log auto remove invalid character on update wsgi bug fixed remove dukpy windows install bug support macosx 0 5 x support plugin storage port scan when wiz project created wiz based online plugin development env support programmable api for plugins remove useless resources socketio config config socketio py packages version bug fixed jinja2 werkzeug add src folder for tracing plugin code check installed function wiz installed forced dev mode in dev branch if not master wiz resource handler updated add function response flask resp and pil image at response add babel script option add wiz path function git merge bug fixed update wiz theme render logic git merge logic changed wiz instance as global in wiz api add match api at wiz instance 0 4 x integrate wiz season flask support git flow workspace structure changed base code workspace changed mysql to filesystem ui upgrade support installer developer production mode developer enabled socketio logger on every pages production disabled socketio logger dictionary bug fixed in app html history display ui changed workspace app browse in route workspace add cache clean in workspace git bug changed if author is not set default user to wiz full size log viewer keyword changed cache bug fixed socketio performance upgrade wiz js embeded wiz api js changed async mode 0 3 x add socket io framework on build command run modified add pattern ignores change framework object 0 2 x framework structure upgraded command line tool function changed submodule structure added logging simplify public directory structure add response template from string function add response template function add variable expression change option interface loader update config onerror changed add response abort error handler in controller error response redirect update relative module path logger upgrade file trace bug fixed logger upgrade log executed file trace logger upgrade code trace error handler bug fixed apache wsgi bug fixed public app py apache wsgi bug fixed | python web-framework wsgi | front_end |
LawBench | div align center img width 540 height 160 src figs lawbench logo png div h1 align center benchmarking legal knowledge of large language models h1 center p align center a href https opencompass org cn target blank website a a href https huggingface co opencompass target blank hugging face a a href https github com open compass lawbench tree main data target blank data a a href https arxiv org abs 2309 16289 target blank paper a p p align center a href https github com open compass lawbench blob main readme md a a href https github com open compass lawbench blob main readme en md english a p llms b lawbench b a href https arxiv org abs 2309 16289 target blank a b lawbench a href https github com hazyresearch legalbench a b lawbench 20 51 lawbench 20 22 9 20 3 500 table class tg thead tr th class tg 0pky th th class tg 0pky id th th class tg 0pky th th class tg 0pky th th class tg 0pky th th class tg 0pky th tr thead tbody tr td class tg lboi rowspan 2 b b td td class tg qdov 1 1 td td class tg qdov td td class tg qdov a href https flk npc gov cn flk a td td class tg qdov rouge l td td class tg qdov td tr tr td class tg 0pky 1 2 td td class tg qdov td td class tg 0pky a href https jecqa thunlp org jec qa a td td class tg 0pky accuracy td td class tg 0pky td tr tr td class tg lboi rowspan 10 b b td td class tg 0pky 2 1 td td class tg 0pky td td class tg 0pky a href http cail cipsc org cn task summit html raceid 2 cail tag 2022 cail2022 a td td class tg 0pky f0 5 td td class tg 0pky td tr tr td class tg 0pky 2 2 td td class tg 0pky td td class tg 0pky a href https laic cjbdi com laic2021 a td td class tg 0pky f1 td td class tg 0pky td tr tr td class tg 0pky 2 3 td td class tg 0pky td td class tg 0pky a href https aistudio baidu com datasetdetail 181754 aistudio a td td class tg 0pky f1 td td class tg 0pky td tr tr td class tg 0pky 2 4 td td class tg 0pky td td class tg 0pky a href https github com liuhuanyong crimekgassitant crimekgassitant a td td class tg 0pky accuracy td td class tg 0pky td tr tr td class tg 0pky 2 5 td td class tg 0pky td td class tg 0pky a href http cail cipsc org cn task summit html raceid 1 cail tag 2019 cail2019 a td td class tg 0pky rc f1 td td class tg 0pky td tr tr td class tg 0pky 2 6 td td class tg 0pky td td class tg 0pky a href https github com china ai law challenge cail2022 tree main xxcq cail2022 a td td class tg 0pky soft f1 td td class tg 0pky td tr tr td class tg 0pky 2 7 td td class tg 0pky td td class tg 0pky a href http cail cipsc org cn task summit html raceid 4 cail tag 2022 cail2022 a td td class tg 0pky rouge l td td class tg 0pky td tr tr td class tg 0pky 2 8 td td class tg qdov td td class tg 0pky a href http cail cipsc org cn task summit html raceid 5 cail tag 2022 cail2022 a td td class tg 0pky accuracy td td class tg 0pky td tr tr td class tg 0pky 2 9 td td class tg qdov td td class tg 0pky a href https github com thunlp leven leven a td td class tg 0pky f1 td td class tg 0pky td tr tr td class tg 0pky 2 10 td td class tg qdov td td class tg 0pky a href https github com thunlp leven leven a td td class tg 0pky soft f1 td td class tg 0pky td tr tr td class tg lboi rowspan 8 b b td td class tg 0pky 3 1 td td class tg 0pky td td class tg 0pky a href https github com china ai law challenge cail2018 cail2018 a td td class tg 0pky f1 td td class tg 0pky td tr tr td class tg 0pky 3 2 td td class tg 0pky td td class tg 0pky a href https github com liuhc0428 law gpt lawgpt zh project a td td class tg 0pky rouge l td td class tg 0pky td tr tr td class tg 0pky 3 3 td td class tg 0pky td td class tg 0pky a href https github com china ai law challenge cail2018 cail2018 a td td class tg 0pky f1 td td class tg 0pky td tr tr td class tg 0pky 3 4 td td class tg 0pky td td class tg 0pky a href https github com china ai law challenge cail2018 cail2018 a td td class tg 0pky normalized log distance td td class tg 0pky td tr tr td class tg 0pky 3 5 td td class tg 0pky td td class tg 0pky a href https github com china ai law challenge cail2018 cail2018 a td td class tg 0pky normalized log distance td td class tg 0pky td tr tr td class tg 0lax 3 6 td td class tg 0lax td td class tg 0lax a href https jecqa thunlp org jec qa a td td class tg 0lax accuracy td td class tg 0lax td tr tr td class tg 0lax 3 7 td td class tg 0lax td td class tg 0lax a href https laic cjbdi com laic2021 a td td class tg 0lax accuracy td td class tg 0lax td tr tr td class tg 0lax 3 8 td td class tg 0lax td td class tg 0lax a href https www 66law cn hualv com a td td class tg 0lax rouge l td td class tg 0lax td tr tbody table data https github com open compass lawbench tree main data task id json json load json 3 2 json instruction question answer predictions zero shot https github com open compass lawbench tree main predictions zero shot predictions one shot https github com open compass lawbench tree main predictions one shot task id json json load json gpt 4 3 2 json 0 origin prompt role human prompt n prediction refr 51 table class tg thead tr th class tg 0pky model th th class tg 0pky parameters th th class tg 0pky sft th th class tg 0pky rlhf th th class tg 0lax access th th class tg 0lax base model th tr thead tbody tr td class tg 9wq8 colspan 8 b multilingual llms b td tr tr td class tg za14 mpt td td class tg za14 7b td td class tg 0pky 10007 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax td tr tr td class tg za14 mpt instruct td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax mpt 7b td tr tr td class tg za14 llama td td class tg za14 7 13 30 65b td td class tg 0pky 10007 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax td tr tr td class tg za14 llama 2 td td class tg za14 7 13 70b td td class tg 0pky 10007 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax td tr tr td class tg za14 llama 2 chat td td class tg za14 7 13 70b td td class tg 0pky 10003 td td class tg 0pky 10003 td td class tg 0lax weights td td class tg 0lax llama 2 7 13 70b td tr tr td class tg 0pky alpaca v1 0 td td class tg 0pky 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 7b td tr tr td class tg za14 vicuna v1 3 td td class tg za14 7 13 33b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 7 13 33b td tr tr td class tg za14 wizardlm td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 7b td tr tr td class tg za14 stablebeluga2 td td class tg za14 70b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 2 70b td tr tr td class tg za14 chatgpt td td class tg za14 n a td td class tg 0pky 10003 td td class tg 0pky 10003 td td class tg 0lax api td td class tg 0lax td tr tr td class tg za14 gpt 4 td td class tg za14 n a td td class tg 0pky 10003 td td class tg 0pky 10003 td td class tg 0lax api td td class tg 0lax td tr tr td class tg 9wq8 colspan 8 b chinese oriented llms b td tr tr td class tg 7zrl moss moon td td class tg 7zrl 16b td td class tg 0lax 10007 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl td tr tr td class tg 7zrl moss moon sft td td class tg 7zrl 16b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl moss moon td tr tr td class tg 0lax tigerbot base td td class tg 0lax 7b td td class tg 0lax 10007 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 0lax td tr tr td class tg 0lax tigerbot sft td td class tg hx86 7b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 0lax tigerbot base td tr tr td class tg 7zrl gogpt td td class tg 7zrl 7b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 0lax llama 7b td tr tr td class tg 7zrl chatglm2 td td class tg 7zrl 6b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl chatglm td tr tr td class tg za14 ziya llama td td class tg za14 13b td td class tg 0pky 10003 td td class tg 0pky 10003 td td class tg 0lax weights td td class tg 0lax llama 13b td tr tr td class tg 7zrl baichuan td td class tg 7zrl 7 13b td td class tg 0lax 10007 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl td tr tr td class tg 7zrl baichuan 13b chat td td class tg 7zrl 13b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl baichuan 13b td tr tr td class tg 7zrl xverse td td class tg 7zrl 13b td td class tg 0lax 10007 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl td tr tr td class tg 7zrl internlm td td class tg 7zrl 7b td td class tg 0lax 10007 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl td tr tr td class tg 7zrl internlm chat td td class tg 7zrl 7b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl internlm 7b td tr tr td class tg 7zrl internlm chat 7b 8k td td class tg 7zrl 7b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl internlm 7b td tr tr td class tg 7zrl qwen td td class tg 7zrl 7b td td class tg 0lax 10007 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl td tr tr td class tg 7zrl qwen 7b chat td td class tg 7zrl 7b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl qwen 7b td tr tr td class tg 7zrl yulan chat 2 td td class tg 7zrl 13b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl llama 2 13b td tr tr td class tg za14 belle llama 2 td td class tg za14 13b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 2 13b td tr tr td class tg 7zrl chinese llama 2 td td class tg 7zrl 7b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 0lax llama 2 7b td tr tr td class tg za14 chinese alpaca 2 td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 2 7b td tr tr td class tg za14 llama 2 chinese td td class tg za14 7 13b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama 2 7 13b td tr tr td class tg baqh colspan 8 span style font weight 400 font style normal b legal specific llms b span td tr tr td class tg za14 hanfei td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax hanfei td tr tr td class tg za14 lawgpt 7b beta1 0 td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax chinese llama td tr tr td class tg za14 lawgpt 7b beta1 1 td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax chinese alpaca plus 7b td tr tr td class tg za14 lexilaw td td class tg za14 6b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax chatglm 6b td tr tr td class tg za14 wisdom interrogatory td td class tg za14 7b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax baichuan 7b td tr tr td class tg za14 fuzi mingcha td td class tg za14 6b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax chatglm 6b td tr tr td class tg za14 lawyer llama td td class tg azeh 13b td td class tg 0pky 10003 td td class tg 0pky 10007 td td class tg 0lax weights td td class tg 0lax llama td tr tr td class tg 7zrl chatlaw td td class tg 7zrl 13 33b td td class tg 0lax 10003 td td class tg 0lax 10007 td td class tg 0lax weights td td class tg 7zrl ziya llama 13b anima 33b td tr tbody table 1 zero shot 2 one shot one shot zero shot 51 zero shot div align center img src figs 0 shot png div 5 gpt 3 5 turbo 2023 6 13 gpt 3 5 turbo task id gpt4 gpt 3 5 turbo stablebeluga2 qwen 7b chat internlm chat 7b 8k avg 52 35 42 15 39 23 37 00 35 73 1 1 15 38 15 86 14 58 18 54 15 45 1 2 55 20 36 00 34 60 34 00 40 40 2 1 12 53 9 10 7 70 22 56 22 64 2 2 41 65 32 37 25 57 27 42 35 46 2 3 69 79 51 73 44 20 31 42 28 96 2 4 44 00 41 20 39 00 35 00 35 60 2 5 56 50 53 75 52 03 48 48 54 13 2 6 76 60 69 55 65 54 37 88 17 95 2 7 37 92 33 49 39 07 36 04 27 11 2 8 61 20 36 40 45 80 24 00 36 20 2 9 78 82 66 48 65 27 44 88 62 93 2 10 65 09 39 05 41 64 18 90 20 94 3 1 52 47 29 50 16 41 44 62 34 86 3 2 27 54 31 30 24 52 33 50 19 11 3 3 41 99 35 52 22 82 40 67 41 05 3 4 82 62 78 75 76 06 76 74 63 21 3 5 81 91 76 84 65 35 77 19 67 20 3 6 48 60 27 40 34 40 26 80 34 20 3 7 77 60 61 20 56 60 42 00 43 80 3 8 19 65 17 45 13 39 19 32 13 37 one shot 51 one shot div align center img src figs 1 shot png div 5 task id gpt4 gpt 3 5 turbo qwen 7b chat stablebeluga2 internlm chat 7b 8k avg 53 85 44 52 38 99 38 97 37 28 1 1 17 21 16 15 17 73 15 03 15 16 1 2 54 80 37 20 28 60 36 00 40 60 2 1 18 31 13 50 25 16 8 93 21 64 2 2 46 00 40 60 27 40 15 00 36 60 2 3 69 99 54 01 32 96 41 76 30 91 2 4 44 40 41 40 31 20 38 00 33 20 2 5 64 80 61 98 46 71 53 55 54 35 2 6 79 96 74 04 57 34 64 99 26 86 2 7 40 52 40 68 42 58 45 06 30 56 2 8 59 00 37 40 26 80 37 60 30 60 2 9 76 55 67 59 50 63 65 89 63 42 2 10 65 26 40 04 21 27 40 54 20 69 3 1 53 20 30 81 52 86 16 87 38 88 3 2 33 15 34 49 34 49 32 44 28 70 3 3 41 30 34 55 39 91 23 07 42 25 3 4 83 21 77 12 78 47 75 80 67 74 3 5 82 74 73 72 73 92 63 59 71 10 3 6 49 60 31 60 26 80 33 00 36 20 3 7 77 00 66 40 44 60 56 00 44 00 3 8 19 90 17 17 20 39 16 24 12 11 evaluation evaluation functions https github com open compass lawbench tree main evaluation evaluation functions 1 f 2 id 3 python main py i f o metric result data system 1 1 1 json 1 2 json system 2 1 1 json 1 2 json metric result 51 zero shot predictions zero shot https github com open compass lawbench tree main predictions zero shot cd evaluation python main py i predictions zero shot o predictions zero shot results csv predictions zero shot results csv https github com open compass lawbench tree main predictions zero shot results csv csv task model name score abstention rate rouge chinese 1 0 3 cn2an 0 5 22 ltp 4 2 13 opencc 1 1 6 python levenshtein 0 21 1 pypinyin 0 49 0 tqdm 4 64 1 timeout decorator 0 5 0 lawbench https github com open compass lawbench blob main readme en md task list rouge l lawbench citation bibtex article fei2023lawbench title lawbench benchmarking legal knowledge of large language models author fei zhiwei and shen xiaoyu and zhu dawei and zhou fengzhe and han zhuo and zhang songyang and chen kai and shen zongwen and ge jidong journal arxiv preprint arxiv 2309 16289 year 2023 | benchmark chatgpt law llm | ai |
cp-web | commercial paper demo deploy to bluemix https bluemix net deploy button png https bluemix net deploy repository https github com ibm blockchain cp web git notice this demo will only ever support hyperledger fabric v0 6 if you are interested in seeing the latest that hyperledger fabric has to offer see the marbles demo https github com ibm blockchain marbles description this application is a demonstration of how a commercial paper trading network might be implemented on ibm blockchain the components of the demo are an interface for creating new users on the network an interface for creating new commercial papers to trade a trade center for buying and selling existing trades a special interface just for auditors of the network to examine trades getting started 1 deploy the demo to your ibm bluemix https www bluemix net account using the button above or you need to create a blockchain network to run this demo you have two options option 1 create a bluemix ibm blockchain network instructions docs use bluemix hyperledger md 1 clone this repository 2 create an instance of the ibm blockchain service in the bluemix catalog 3 copy the credentials from the service into the file mycreds json 4 make sure the key value store only has values for your current network see below 5 run these commands in the cloned directory shell npm install gulp these credentials can be obtained from the service credentials tab of the bluemix service they are in the form json credentials peers discovery host 169 53 62 121 discovery port 40275 api host 169 53 62 121 api port 40276 type peer network id 4b21f2f9 4d10 4946 a0df f91ac09dbc03 id 4b21f2f9 4d10 4946 a0df f91ac09dbc03 vp1 api url http 169 53 62 121 40276 users username user type0 b7c7a1e545 secret 89ce33e4e6 option 2 use a locally hosted hyperledger network such as one from docker compose 1 clone this repository 2 make sure your key value store only has values for your current network see below 3 follow these instructions docs use local hyperledger md to stand up a local blockchain network and configure the demo to use it 4 run these commands in the cloned directory shell npm install gulp to debug the code run these commands shell npm install debug hfc grpc trace all gulp using the demo 1 register some users using the registration form on the login page 2 save the credentials that are created for the users you register they appear just above the registration form 3 use the credentials to log in to the application the ui you see will be determined by the role that was assigned to each user 4 open the create tab to create new trades 5 open the trade tab to participate in your commercial paper trading network 6 open the audit tab to view all of the trades on the network to access the auditor tab create a user with auditor in their enrollid ex myauditor auditor34 etc login as this user and you will see the audit tab notes on the key value store when the fabric sdk is used to enroll users the enrollment certificate for the user is downloaded from the ca and the secret for the user you enrolled is invalidated basically nobody will be able to enroll the user again by default the sdk will download this certificate into a local key value store so the only apps that will be able to use the enrolled users are those that have access to the enrollment certificate why is this a problem when this demo is initialized it attempts to enroll one of the blockchain networks registrar users webappadmin downloading the enrollment certificate for that user into the demo applications filesystem on bluemix this will prevent other demos or apps on that network from being able to use the webappadmin user the message to take away from all of this is that you should only use this demo on it s own blockchain network for now limitations passwords don t mean anything in the demo we re working on it you still need to register users to interact with the demo but you can use anything in the password field to log in having the string auditor in the username will cause the user to be registered as an auditor while anything else will register the user as a regular user meaning that they can create and trade paper these limitations will be fixed in future versions of the demo nothing happens when papers mature for now the permissions for auditors and regular users are only enforced within the web application an updated user architecture will be coming to the fabric to fix this in the future privacy notice this web application includes code to track deployments to ibm bluemix https www bluemix net and other cloud foundry platforms the following information is sent to a deployment tracker https github com cloudant labs deployment tracker service on each deployment application name application name space id space id application version application version application uris application uris this data is collected from the vcap application environment variable in ibm bluemix and other cloud foundry platforms this data is used by ibm to track metrics around deployments of sample applications to ibm bluemix to measure the usefulness of our examples so that we can continuously improve the content we offer to you only deployments of sample applications that include code to ping the deployment tracker service will be tracked disabling deployment tracking deployment tracking can be disabled by deleting the following code in app js javascript track the application deployments require cf deployment tracker client track troubleshooting solutions for common problems with running this demo locally are included below npm install fails with node gyp errors in the output first make sure you are running this demo with the latest lts versions of node js and npm you can check your versions of these two tools using these commands bash node v v6 9 1 npm v 3 10 8 second this demo uses modules that must be compiled which requires you to have certain build tools on your machine if you are running on windows you should install the package here https github com felixrieseberg windows build tools finally delete the node modules folder and give npm install another try error creating deployment archive do your logs have a message similar to this one text chain setup js failed to deploy chaincode eventtransactionerror error error error creating deployment archive tmp deployment package tar gz error error on fs createwritestream at error native at c users ibm admin documents obc git demos cp web node modules hfc lib hfc js 1411 31 at writestream anonymous c users ibm admin documents obc git demos cp web node modules hfc lib sdk util js 163 16 at emitone events js 101 20 at writestream emit events js 188 7 at writestream anonymous fs js 2109 12 at fsreqwrap oncomplete fs js 123 15 msg error error creating deployment archive tmp deployment package tar gz error error on fs createwritestream chain setup js chaincode deployment failed undefined this often happens because the tmp directory is not present on your machine hfc uses this folder to temporarily store and package this demo s chaincode for deployment create the directory and you should be fine this directory will be c tmp on windows machines | blockchain |
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news.12bit.vn | news 12bit vn browse multiple web development sites at the same time netlify status https api netlify com api v1 badges 51d69537 8760 4eef ad70 fead35768f13 deploy status https app netlify com sites jovial spence 90afc6 deploys featured image https user images githubusercontent com 3280351 56430895 1a0a4880 62f2 11e9 98b2 d3bb66ae80ab png contributing prerequisites since netlify does not support hugo extended version that allows hugo converts scss to css we need to use a task runner to convert scss in this case webpack is our first choice hugo node js npm install node js dependencies yarn install start webpack development local server you may need to run the both commands at the same time yarn dev hugo serve browse the site at http localhost 1313 add your site trust me it s really simple you don t need to inbox us if you have a blog you can add your rss link into this file https github com 12bitvn news 12bit vn blob master data links json then create a pull request we ll review it as soon as possible | hugo-site hugo netlify netlify-lambda netlify-functions | front_end |
stanford-cars | standford cars image classification binder https mybinder org badge logo svg https mybinder org v2 gh morganmcg1 projects master launch binder or share the binder link https mybinder org v2 gh morganmcg1 projects master image classification of the stanford cars dataset leveraging the fastai v1 the goal is to try hit 90 accuracy shoot for the stars starting with a basic fastai image classification workflow and interating from there this was all run on a paperspace p4000 machine apart from the efficientnet b7 results which were run on a p6000 current best score 94 79 sota 96 2 domain adaptive transfer learning with specialist models https arxiv org pdf 1811 07056 pdf tl dr notebook 10 stanford cars efficientnet b7 ranger mish trial ipynb https github com morganmcg1 stanford cars blob master 10 stanford cars efficientnet b7 ranger mish trial ipynb continuing on my efficinetnet b3 result of 93 8 https forums fast ai t project stanford cars with fastai v1 34311 37 i matched the efficinetnet paper s b7 result achieved 94 79 standard deviation of 0 094 5 run 40epoch mean test set accuracy on stanford cars using mish efficientnet b7 ranger matched the efficientnet paper efficientnet b7 result of 94 7 current sota is 96 0 used mefficientnet b3 created by swapping the squish activation function for the mish activation function used the ranger optimisation function a combination of radam and lookahead and trained with flatcosannealscheduler credits huge credit for this work goest to this inspirational fastai thread https forums fast ai t meet mish new activation function possible successor to relu 53299 280 credit to lukemelas for the pytorch implementation and all the fastai community especially lessw2020 for ranger digantamisra98 for mish and muellerzr grankin and everyone else there for making valuable contributions notebook results 10 stanford cars efficientnet b7 ranger mish trial ipynb achieved 94 79 5 run 40epoch mean test set accuracy on stanford cars using mish efficientnet b7 ranger using the mish activation and ranger with efficientnet b7 see notebook for implementation details further discussion of this results can be found on the fastai forums https forums fast ai t project stanford cars with fastai v1 34311 43 full accuracy and validation loss results for each run in the results excel file https github com morganmcg1 stanford cars blob master mefficientnet b7 ranger results xlsx exp stanford cars efficientnet mish range913a ipynb ran 8 experiments testing beta version of new range913a from less20202 https github com lessw2020 ranger deep learning optimizer matched previous best using vanialla ranger 93 8 needed a higher lr to match 1e2 vs 1 5e 3 notebook for full results and plots of validation loss and accuracy 9 stanford cars efficientnet ranger mish trial ipynb achieved 93 8 5 run 40epoch mean test set accuracy on stanford cars using mish efficientnet b3 ranger using the mish activation and ranger with efficientnet b3 see notebook for implementation details validation loss and accuracy i used the test set as the validation set are saved in mefficientnet b3 ranger results xlsx fastai forums post and discussion https forums fast ai t project stanford cars with fastai v1 34311 37 quick medium post https medium com morganmcg 6 stanford cars cutout ipynb used the cuout data augmentation alongside default fastai data transforms size of the squares were 25 of the image side e g 25 x 224 88 3 accuracy achieved 5 stanford cars mixup and dropout ipynb tuning the dropout parameters while also using the mixup https arxiv org abs 1710 09412 protocol 89 2 accuracy achieved with agressive dropout ps 0 35 0 7 accuracy more or less the same as nb4 4 stanford cars mixup ipynb tuning the model using the mixup https arxiv org abs 1710 09412 protocol blending input images to provide stronger regularisation 89 4 accuracy up 1 since nb2 3 stanford cars cropped ipynb training the model using the cropped images based on the bounding boxes provided 78 54 accuracy down 9 5 from notebook 2 2 stanford cars lr tuning ipynb tuning of the learning rate and differential learning rates again using fastai s implementation of the 1 cycle policy 88 19 accuracy up 3 2 1 stanford cars basic ipynb benchmark model using basic fastai image classification workflow including the 1 cycle policy 84 95 accuracy labels df csv contains the labels filepath and test train flag for each image file s0ta 95 ws dan see better before looking closer weakly supervised data augmentation network for fine grained visual classification hu 2019 https arxiv org abs 1901 09891 code might not be released until october 2019 if it is accepted for iccv 2019 previous sota 93 61 apr 18 https www researchgate net publication 316027349 deep cnns with spatially weighted pooling for fine grained car recognition potential avenues of investigation fine tune first on cars from google open images https storage googleapis com openimages web download html use dat domain adaptive transfer learning with specialist models https arxiv org pdf 1811 07056 pdf fornax great roundup in advances in 2018 some of which can be applied https github com kmkolasinski deep learning notes blob master seminars 2018 12 improving dl with tricks improving deep learning models with bag of tricks pdf amazon bag of tricks for image classification with convolutional neural network https arxiv org pdf 1812 01187 pdf multi attention cnn https github com jianlong fu multi attention cnn visualise with images in t sne https github com kheyer ml dl projects data augmentation great visualisaton here for the transforms available in fastai https www kaggle com init27 introduction to image augmentation using fastai train only on cropped images use mixup paper https arxiv org abs 1710 09412 paper repo https github com facebookresearch mixup cifar10 fastai docs https docs fast ai callbacks mixup html https forums fast ai t mixup data augmentation 22764 21 mixup dropout produced good results in mixup paper adamixup https arxiv org abs 1809 02499v3 cutout improved regularization of convolutional neural networks with cutout https arxiv org pdf 1708 04552 pdf https docs fast ai vision transform html cutout rgb transforms which i tested here https github com morganmcg1 projects blob master feature testing rgb 20transformation 20testing ipynb label smoothing https arxiv org abs 1512 00567 mixup label smoothing tested in mixup paper maybe not didn t produce great results random erasing zhong et al 2017 paper https arxiv org abs 1708 04896 try increase zoom and higher resolution images use own stats mean std dev from training set to normalize the images use non standard fastai image augmentations including augmentations for this dataset can be found here http ee sharif edu shayan f fgcc index html training regimes agressive lr for training all layers adding weight decay and tuning dropout batch norm sgugger s adam experiments https github com sgugger adam experiments adamw with 1 cycle https twitter com radekosmulski status 1014964816337952770 s 12 adamw and other dl tricks https twitter com drsxr status 1073208269907353602 s 12 train with bn freeze true for unfrozen layers shake shake regularisation mentioned in fornax slides above knowledge distillation paper https arxiv org abs 1503 02531 https forums fast ai t part 1 complete collection of video timelines 5504 kaggle winner mixup knowledge distillation http arxiv org abs 1809 04403v2 architecture try alternate resnet sizes benchmark used resnet152 try alternate archs e g densenet unet try xresnet152 https twitter com jeremyphoward status 1115036889818341376 s 12 credits code to extract the labels and annotations from the mat files devon yates code on kaggle thanks devon https www kaggle com criticalmassacre inaccurate labels in stanford cars data set my 90 goal was based on sgugger s code implementing adam for the stanford cars dataset here https github com sgugger adam experiments | ai |
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kc-ios2 | madrid shopping pr ctica de ios avanzado con swift 4 del v keepcoding startup engineering master bootcamp 2017 goals create a mobile application to display information of shops in madrid even when the user has no internet connection shops should be shown in a map requirements 1 when starting the app for the first time if there s internet connection it will download all information from the shops access point see below including all images 2 the app will cache everything locally images data etc even images of the maps see below for tips 3 cache is never invalidated so once everything has been saved set a flag and never ever access to the network again 4 if there s no internet connection a message will be shown to the user 5 while caching the app will show an activity indicator or other loader until you finish caching you don t get to the main menu 6 the app will have a main menu screen where we ll add one button a logo the button takes us to the list of shops 7 the list of shops will show in the upper 50 screen a map with one pin for each shop 8 the list of shops will show in the lower 50 screen logo to the left background image taking all the row shop name in the front tapping a row takes us to the detail shop screen 9 all info should be read from a core data database 10 if you tap on a pin in the map a callout will open with the logo shop name taping the callout takes us to the detail shop screen 11 the map will be always centered in madrid showing also the user location 12 all data is at least in spanish english should cache all and display in spanish if that s our phone s language or english otherwise 13 shop detail screen should show shop name description address and a map showing the shops s location without any pin installation to install the application execute the next commands git clone https github com smarrerof kc ios2 cd kc ios2 open madridshops xcodeproj run and enjoy the app in your favorite emulator or in your device the app has been tested in ios 10 and ios11 1 coredata is used as cache shops and actvities are saved in coredata when the app runs for the firt time logo image and static map for each shop activity are stored in coredata as well 2 shop activity list and detail is shown with only one viewcontroller access to coredate is managed by a class that uses a generic class to access to the correct table this class can be extended to accept filters 3 cocoapod is not used so access to network is managed by a custom class 4 a red border is added as background image to each cell 5 download and cache shops and activities are is custom classes because each entity can download more information that the information that is abstracted in the entity model classes at this moment this information is the same but more viewcontrollers can be added to use this extended information demo madridshops demo https raw githubusercontent com smarrerof kc ios2 master madridapp ios gif | ios swift coredata | os |
ocaml-bert | ocaml bert bert like nlp models implemented in ocaml using the ocaml torch https github com laurentmazare ocaml torch pytorch bindings these are based on the rust bert https github com guillaume be rust bert implementation which itself is a port of huggingface s transformers https github com huggingface transformers to rust the models implemented so far are distillbert pre trained weights are available under the apache 2 0 license from huggingface weights file https cdn huggingface co distilbert base uncased rust model ot vocabulary file https cdn huggingface co bert base uncased vocab txt bert pre trained weights are available under the apache 2 0 license from google and hosted by huggingface weights file https cdn huggingface co bert base uncased rust model ot vocabulary file https cdn huggingface co bert base uncased vocab txt | ai |
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Data-Science-Cheatsheet | data science cheatsheet 2 0 a helpful 5 page data science cheatsheet to assist with exam reviews interview prep and anything in between it covers over a semester of introductory machine learning and is based on mit s machine learning courses 6 867 and 15 072 the reader should have at least a basic understanding of statistics and linear algebra though beginners may find this resource helpful as well inspired by maverick s data science cheatsheet hence the 2 0 in the name located here https github com ml874 data science cheatsheet topics covered linear and logistic regression decision trees and random forest svm k nearest neighbors clustering boosting dimension reduction pca lda factor analysis natural language processing neural networks recommender systems reinforcement learning anomaly detection time series a b testing this cheatsheet will be occasionally updated with new improved info so consider a follow or star to stay up to date future additions ideas welcome time series added statistics and probability added data imputation generative adversarial networks graph neural networks links data science cheatsheet 2 0 pdf https github com aaronwangy data science cheatsheet blob main data science cheatsheet pdf screenshots here are screenshots of a couple pages the link to the full cheatsheet is above images page1 1 png raw true images page2 1 png raw true why is python sql not covered in this cheatsheet i planned for this resource to cover mainly algorithms models and concepts as these rarely change and are common throughout industries technical languages and data structures often vary by job function and refreshing these skills may make more sense on keyboard than on paper license feel free to share this resource in classes review sessions or to anyone who might find it helpful this work is licensed under the a rel license href http creativecommons org licenses by nc sa 4 0 creative commons attribution noncommercial sharealike 4 0 international license a a rel license href http creativecommons org licenses by nc sa 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by nc sa 4 0 88x31 png a br images are used for educational purposes created by me or borrowed from my colleagues here https stanford edu shervine teaching cs 229 contact feel free to suggest comments updates and potential improvements author aaron wang https www linkedin com in axw if you d like to support this cheatsheet you can buy me a coffee here https www paypal me aaxw i also do resume application and tech consulting send me a message if interested | cheatsheet data-science machine-learning | ai |
enterprise_blockchain_tutorial | enterprise blockchain tutorial div align center pic cover 7 png div https www ixueshu com document cd7838079876fcadc84d23b8ec161b67318947a18e7f9386 html 16 5 2019 https www sohu com a 364826594 505899 https item jd com 12719282 html 2019 11 div align center pic qrcode jpg 0 0 pic book jpg 0 1 pic project jpg 0 2 div 2019 10 24 http www gov cn xinwen 2019 10 25 content 5444957 htm div align center pic learn png 0 3 div 2020 hyperledger linux 275 ibm div align center pic hyperledger member png 0 4 div hyperledger fabric fabric ibm dah 2015 hyperledger fabric hyperledger fabric hyperledger fabric 2 linux docker git 1 chapter1 01 20blockchain core concept md chapter1 02 20types 20of blockchains md chapter1 03 20correct method of learn blockchain md chapter1 04 20how does blockchain work md chapter1 05 20blockchain 20application 20principle md chapter1 06 20blockchain project execution md chapter1 07 20summary md 2 chapter2 01 20misunderstands of blockchain md chapter2 02 20the ability of dev md chapter2 03 20three questions of dev md chapter2 04 20blockchain engineer skills md chapter2 05 20selection of technology md chapter2 06 20summary md 3 hyperledger fabric hperledger chapter3 01 20hyperledger project overview md hyperledger fabric chapter3 02 part1 20hyperledger fabric network prerequisites md hyperledger fabric fabric chapter3 02 part2 20hyperledger fabric network installing md hyperledger fabric chapter3 02 part3 20hyperledger fabric network bring up md hyperledger fabric chapter3 03 20hyperledger fabric architecture md hyperledger fabric chapter3 04 20hyperledger fabric core components md hyperledger fabric chapter3 05 20hyperledger fabric workflow of transaction md hyperledger fabric chapter3 06 20summary md 4 fabric 5 orderer 4 peer 1 cli chapter4 01 20fabric network overview md chapter4 02 20generate certificate md chapter4 03 20configuration transaction md chapter4 04 20bring up network md chapter4 05 20create join channel md chapter4 06 20test fabric network md chapter4 07 summary md 5 chapter5 01 20smartcontract dev env md golang chapter5 02 part1 20golang core md golang chapter5 02 part2 20golang core md golang chapter5 02 part3 20golang core md golang chapter5 02 part4 20golang core md chapter5 03 20chaincode concept md chapter5 04 20chaincode dev md chapter5 05 20chaincode unit test md chapter5 06 20summary md 6 chapter6 01 20solution md chapter6 02 20requirement md chapter6 03 20asset business design md chapter6 04 20smartcontract dev md fabric sdk chapter6 05 20fabric sdk md chapter6 06 20blockchain application dev md chapter6 07 20summary md 7 ibm bluemix chapter7 01 20ibm bluemix baas md chapter7 02 20aliyun baas md cello chapter7 03 20cello md chapter7 04 20summary md 8 chapter8 01 20blockchain technology integration md chapter8 02 20blockchain technology challenges md chapter8 03 20blockchain technology trend md chapter8 04 20summary md 9 chapter9 00 md chapter9 00 md 1 https item jd com 12719282 html 2019 2 fabric https hyperledger fabric readthedocs io en release 1 4 div align center pic payment code png pic alipay png div | blockchain blockchain-technology blockchain-platform hyperledger hyperledger-fabric hyperledger-fabric-network hyperledger-fabric-sdk | blockchain |
libhmemory | hmemory hmemory is a memory error detector for c c programs 1 a href 1 overview overview a 2 a href 2 configuration configuration a a href 21 compile time options compile time options a a href 22 run time options run time options a 3 a href 3 error reports error reports a 4 a href 4 test cases test cases a 5 a href 5 usage example usage example a a href 51 build hmemory build hmemory a a href 52 memory leak memory leak a a href 53 memory corruption memory corruption a 6 a href 6 contact contact a 7 a href 7 license license a 1 overview hmemory is a lightweight memory error detector for c c programs specifically designed for embedded systems main use case may include embedded systems where a href http valgrind org valgrind a a href http valgrind org docs manual ac manual html addrcheck a or a href http valgrind org docs manual mc manual html memcheck a support is not available and has benefits of has a negligible effect run time speed does not require any source code change operating system and architecture independent easy to use can detect errors of double invalid free mismatched use of malloc versus free writing before or end of malloc d blocks invalid realloc overlapping src and dst pointers in memcpy memory leaks 2 configuration 1 a href 21 compile time options compile time options a 2 a href 22 run time options run time options a 2 1 compile time options hmemory configuration parameters can be set using tt make flags tt please check example section for demonstration hmemory enable callstack default 0 enable disable reporting call trace information on error useful but depends on tt libbdf tt tt libdl tt and tt backtrace function from glibc tt may be disabled for toolchains which does not support backtracing hmemory report callstack default 0 dump callstack info function call history for error point hmemory assert on error default 1 terminate the process on any pthreads api misuse and or lock order violation hmemory corruption check interval default 5000 memory corruption checking interval in miliseconds use 1 to disable hmemory show reachable default 0 show reachable memory on exit 2 2 run time options hmemory reads configuration parameters from environment via getenv function call one can either set change environment variables in source code of monitored project via setenv function call or set them globally in running shell using export function please check example section for demonstration hmemory report callstack default 0 dump callstack info function call history for error point hmemory assert on error default 1 terminate the process on any pthreads api misuse and or lock order violation hmemory corruption check interval default 5000 memory corruption checking interval in miliseconds use 1 to disable hmemory show reachable default 0 show reachable memory on exit 3 error reports 4 test cases 5 usage example 1 a href 51 build hmemory build hmemory a 2 a href 52 memory leak memory leak a 3 a href 53 memory corruption memory corruption a using hmemory is pretty simple just clone libhmemory and build add tt include hmemory h dhmemory debug 1 g o1 tt to target cflags link with tt lhmemory lpthread lrt tt if hmemory enable callstack is 0 or link with tt lhmemory lpthread lrt ldl lbfd tt if hmemory enable callstack is 1 5 1 build hmemory compile libhmemory with callstack support git clone git github com anhanguera libhmemory git cd libhmemory hmemory enable callstack 1 make compile libhmemory without callstack support git clone git github com anhanguera libhmemory git cd libhmemory hmemory enable callstack 0 make 5 2 memory leak let below is the source code with memory leak to be monitored 1 include stdio h 2 include stdlib h 3 include string h 4 5 int main int argc char argv 6 7 void rc 8 void argc 9 void argv 10 rc malloc 1024 11 if rc null 12 fprintf stderr malloc failed n 13 exit 1 14 15 rc malloc 1024 16 if rc null 17 fprintf stderr malloc failed n 18 exit 1 19 20 free rc 21 return 0 22 compile and run as usual gcc o app main c app application will exit normal nothing unusual excpet that we have a leaking memory which was allocated at line 10 now enable monitoring with hmemory gcc include src hmemory h dhmemory debug 1 g o1 o app debug main c lsrc lhmemory lpthread ld library path src app debug app debug hmemory 19437 memory information hmemory 19437 current 1032 bytes 0 00 mb hmemory 19437 peak 2064 bytes 0 00 mb hmemory 19437 total 2064 bytes 0 00 mb hmemory 19437 leaks 1 items hmemory 19437 memory leaks hmemory 19437 1032 bytes at main main c 10 assertion failed 0 memory leak function hmemory fini file hmemory c line 775 hmemory will report memory information and leaking points on exit 5 3 memory corruption let below is the source code with memory corruption overflow to be monitored 1 include stdio h 2 include stdlib h 3 include string h 4 include unistd h 5 6 int main int argc char argv 7 8 void rc 9 void argc 10 void argv 11 rc malloc 1024 12 if rc null 13 fprintf stderr malloc failed n 14 exit 1 15 16 memset rc 0 1025 17 sleep 1 18 free rc 19 return 0 20 enable monitoring with hmemory gcc include src hmemory h dhmemory debug 1 g o1 o app debug main c lhmemory lpthread app debug hmemory 19513 free with corrupted address 0x7fb5ab000000 overflow hmemory 19513 at main main c 18 assertion failed rcu 0 rco 0 memory corruption function debug memory check actual file hmemory c line 634 free call after memset catchs the error hmemory has check points in every intercepted call there is also a corruption checker which works with defined interval below is the example of how to tune gcc include src hmemory h dhmemory debug 1 g o1 o app debug main c lhmemory lpthread hmemory corruption check interval 250 app debug hmemory 19530 worker check with corrupted address 0x7f80fb800000 overflow hmemory 19530 at hmemory worker hmemory c 727 assertion failed rcu 0 rco 0 memory corruption function debug memory check actual file hmemory c line 634 since the interval is 250 miliseconds corruption checker could catch before hmemory check points 6 contact if you are using the software and or have any questions suggestions etc please contact with me at alper akcan gmail com 7 license copyright c 2008 2013 alper akcan alper akcan gmail com this work is free it comes without any warranty to the extent permitted by applicable law you can redistribute it and or modify it under the terms of the do what the fuck you want to public license version 2 as published by sam hocevar see the copying file for more details | os |
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cvpce | cvpce computer vision based planogram compliance evaluation code for the master s thesis of julius laitala university of helsinki 2021 the thesis is available at http urn fi urn nbn fi hulib 202106092585 installation currently the functions in cvpce are set up to run only on cuda therefore you ll need a nvidia card to run cvpce we suggest using conda https docs conda io en latest to avoid cuda installation pains to create a conda environment for cvpce simply utilize the provided environment yml environment yml sh conda env create f environment yml conda activate cvpce with the conda environment set up and activated cvpce can be installed with setuptools sh pip install if you wish to tweak cvpce a bit the e flag is your friend usage cvpce is a command line tool and a bunch of usage instructions can be accessed with the help option go ahead and sh cvpce help after installing to explore the available commands pre trained weights pre trained model weights are available in the releases https github com laitalaj cvpce releases datasets the following public datasets were used for training and testing cvpce gln training and product proposal generation testing sku 110k of goldman et al 2019 https github com eg4000 sku110k cvpr19 dihe training the 2019 version of gp 180 https alessiotonioni github io publication dihe tonioni et al 2017 classification and product detection testing the grocery products dataset https github com tobiagru objectdetectiongroceryproducts of george et al 2014 with annotations from gp 180 the 2017 version https alessiotonioni github io publication planogram planogram compliance testing planograms from gp 180 2017 version https alessiotonioni github io publication planogram with fixes from this gist https gist github com laitalaj 09778eab24c0d16b8447d6ca3360c7b2 | ai |
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Annotation_Tools | open source annotation tools for computer vision and nlp tasks computer vision computer vision make sense make sense cvat cvat classif ai classifai coco annotator coco annotator natural language processing natural language processing doccano doccano inception inception rasa bulk labelling demo computer vision make sense makesense ai 1 is a free to use online tool for labelling photos thanks to the use of a browser it does not require any complicated installation just visit the website and you are ready to go it also doesn t matter which operating system you re running on we do our best to be truly cross platform it is perfect for small computer vision deeplearning projects making the process of preparing a dataset much easier and faster prepared labels can be downloaded in one of multiple supported formats documentation and more details can be found in the project repository 2 p align center img src polygon demo gif width 600 p https github com skalskip make sense cvat the computer vision annotation tool cvat is a free online interactive video and image annotation tool for computer vision many ui and ux decisions are based on feedback from professional data annotation teams opencv cvat on github https github com opencv cvat video tutorials introduction video https www youtube com watch v l9 ivuihgwm feature youtu be annotation mode video https www youtube com watch v 6h7hxgl6ct4 feature youtu be interpolation mode video https www youtube com watch v u3mydhesho4 feature youtu be attribute mode video https www youtube com watch v upnfwl8egd8 feature youtu be segmentation mode video https www youtube com watch v fh8okusuips feature youtu be tutorial for polygons video https www youtube com watch v xtwfxdh4cli semi automatic segmentation video https www youtube com watch v vnqxz z vtq p align center img src https github com opencv cvat blob develop cvat apps documentation static documentation images cvat jpg width 600 p https github com opencv cvat classif ai classifai aims to be one of the most comprehensive open source data annotation platforms available it supports the labelling of various data types with multi labelled outputs forms for ai model training plus it runs locally on your computer classifai has native builds for windows linux and macos so it s easy to get started figure below show how classifai fits in the machine learning workflow it enables the labelling of raw data imported from data source the labelled data can then channel into training environments for supervised semi supervised learning p align center img src https github com certifaiai classifai blob main metadata bounding box 0 gif width 600 p https github com certifaiai classifai classifai also comes with a conversion launcher which is useful for optical character recognition ocr currently supports the conversion of format of pdf tif to png jpg links download https classifai ai products source code https github com certifaiai classifai documentation https docs classifai ai coco annotator p align center img src https github com machine learning tokyo annotation tools blob master coco annotator png width 600 p https github com jsbroks coco annotator coco annotator is a web based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection it provides many distinct features including the ability to label an image segment or part of a segment track object instances labeling objects with disconnected visible parts efficiently storing and export annotations in the well known coco format the annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets p align center img src https github com machine learning tokyo annotation tools blob master coco gif width 600 p https github com jsbroks coco annotator coco annotator allows users to directly export to coco format segment the objects to add key points to analyze data through useful api endpoints import datasets already annotated in coco format and many more links wiki https github com jsbroks coco annotator wiki github https github com jsbroks coco annotator natural language processing doccano doccano is an open source text annotation tool for human it provides annotation features for text classification sequence labeling and sequence to sequence you can create labeled data for sentiment analysis named entity recognition text summarization etc https doccano herokuapp com p align center img src https github com machine learning tokyo annotation tools blob master doccano gif width 600 p https github com chakki works doccano 1 http makesense ai 2 https github com skalskip make sense inception a semantic annotation platform offering intelligent assistance and knowledge management it s free and more feature rich than prodi gy https inception project github io p align center img src https inception project github io images screenshot annotation png width 600 p https inception project github io rasa bulk labelling demo a jupyter notebook that uses language embeddings and dimensionality reduction techniques to apply labels in bulk the technique was developed at rasa and is demonstrated in detail on youtube https www youtube com watch v t0ddetqgra4 https github com rasahq rasalit raw main docs bulk gif | ai |
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Blockchain | a href https www linkedin com in bellajbadr img src https img shields io badge support recommend 2fendorse 20me 20on 20linkedin blue style for the badge logo linkedin alt endorse me on linkedin a blockchain and cryptocurrency library collection of useful documents about bitcoin ethereum blockchain technologies and distributed system to contribute contribution are always welcome and recommended here is how fork the repository https help github com articles fork a repo clone to your machine git clone https github com your username blockchain git make your changes create a pull request blockchain books blockchain by example https www packtpub com big data and business intelligence blockchain example https www packtpub com big data and business intelligence blockchain example https d1ldz4te4covpm cloudfront net sites default files imagecache ppv4 main book cover b08754 png related repositories and sources intro to consensus mechanisms https thomasvilhena com 2020 10 a review of consensus protocols br excellent serie of bitcoin article for developers https davidederosa com basic blockchain programming academic publications about bitcoin and blockchain technology https cdecker github io btcresearch 2017 evolution of smart contract security in the ethereum ecosystem https blog zeppelinos org evolution of smart contract security in the ethereum ecosystem a list of ethereum events https github com genesisdotre awesome ethereum events a list of smart contracts platforms https github com overtorment awesome smart contracts academic publications bitcoin ethereum blockchain https blockchainlibrary org category academic publications all about zero knowledge proofs https zkp science ethereum forum for researchers https ethresear ch videos byzantine paxos https www youtube com watch v xnfazhkyoy4 donation btc 1lzzuvcrurakhd1vpjk4u9dgeyhd9ht1sl eth 0xf15b0d353ceb13806d037f5b0165c8480b37e43b miscellaneous links tools ethereum conferences https davidburela wordpress com category blockchain beigepaper a rewrite of the yellowpaper in non yellowpaper syntax https github com chronaeon beigepaper a curated list of awesome ethereum security references https github com trailofbits awesome ethereum security azure blockchain hands on lab step by step https github com microsoft mcw azure blockchain tree master hands on 20lab principles of distributed computing lecture collection https disco ethz ch courses podc allstars analyzing cryptocurrency markets using python https blog patricktriest com cdn ampproject org c s blog patricktriest com analyzing cryptocurrencies python amp gekko define your own trading strategy and gekko will take care of everything else https gekko wizb it ostia ostia is a cryptocurrency trading platform that allows you to run algorithmic trading strategies across all major exchanges https github com adamalawrence ostia market arbitrage https data bitcoinity org markets arbitrage usd prediction bitcoin price variation https github com satvikshetty04 predicting bitcoin price variations predicting bitcoin price variations using bayesian regression https github com intellectape predicting bitcoin price variations using bayesian regression cryptocurrency analysis with python https romanorac github io cryptocurrency analysis 2017 12 29 cryptocurrency analysis with python part3 html blackbird bitcoin arbitrage a c trading system https github com butor blackbird blob master readme md cuckoo cycle the first graph theoretic proof of work https github com tromp cuckoo deos decentralized operating system https github com desantisinc deoscore tribeca a high frequency market making cryptocurrency trading platform in node js https github com michaelgrosner tribeca | bitcoin-ethereum blockchain bitcoin dag hyperledger ethereum whitepapers cryptography cryptocurrencies corda quorum aeternity mchain fractal | blockchain |
kubeflow | img src https www kubeflow org images logo svg width 100 kubeflow the cloud native platform for machine learning operations pipelines training and deployment documentation please refer to the official docs at kubeflow org http kubeflow org working groups the kubeflow community is organized into working groups wgs with associated repositories that focus on specific pieces of the ml platform automl https github com kubeflow community tree master wg automl deployment https github com kubeflow community tree master wg deployment manifests https github com kubeflow community tree master wg manifests notebooks https github com kubeflow community tree master wg notebooks pipelines https github com kubeflow community tree master wg pipelines serving https github com kubeflow community tree master wg serving training https github com kubeflow community tree master wg training quick links pr dashboard https k8s gubernator appspot com pr get involved please refer to the community https www kubeflow org docs about community page | ml kubernetes minikube tensorflow notebook google-kubernetes-engine jupyter machine-learning kubeflow | ai |
Linux-System-Programming | linux system programming this repository contains source code for real time programming course in my bachelor s degree in information technology | server |
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juju-charm-tutorial | juju charm tutorial link for the tutorial https docs jujucharms com 2 4 en authors charm writing | cloud |
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cvlab | cv lab computer vision laboratory a rapid prototyping tool for computer vision algorithms a href https drive google com uc export download id 15g4uplzwxftl5pn53kn1co1yp02lzbgh img align right height 220 src https drive google com uc export download id 15g4uplzwxftl5pn53kn1co1yp02lzbgh a installation installation description description usage usage faq faq known issues issues copyright copyright installation installation using pip pip3 install upgrade cvlab this command will install cv lab or update if you have already installed it see the pypi page https pypi python org pypi cvlab for more information alternatively you can clone entire git repository cv lab requires pyqt5 opencv numpy scipy pygments tinycss2 matplotlib description cv lab enables convenient development of computer vision algorithms by means of graphical designing of the processing flow writing code with opencv might be a time consuming process it is often required to compile and run the code multiple times in order to see the results of the modifications of the algorithm especially when some parameters are to be tuned for establishing the optimal values some code also has to be added to provide presentation of the intermediate or final results of the algorithm instead cv lab offers interactive construction of the algorithms opencv functions are available in a form of a palette of image processing blocks they can be drag n dropped into a diagram and connected to each other for defining the data flow outputs of the functions in the diagram can be previewed parameters are available as convenient widgets like sliders or spinners therefore any change in the diagram or parameter values can be instantly observed in the selected previews homepage on github https github com cvlab ai cvlab pypi package https pypi python org pypi cvlab usage to run cv lab just write in console cvlab or python3 m cvlab or python3 o cvlab main py creating image processing diagram 1 drag drop processing elements from the palette to diagram area 1 connect elements by drag dropping their connectors 1 open output previews by double clicking elements 1 adjust parameters and see the outputs moving around the diagram 1 use middle mouse button or mouse wheel to scroll the diagram 1 select single element by clicking on it 1 select multiple elements by clicking and dragging on the diagram area 1 move elements with drag drop displaying output images or data 1 double click on the element to open data previews 1 use mouse wheel on the previews to zoom in out 1 double click on the preview to open external window with additional preview writing python code using cv lab 1 put code element on the diagram and connect its inputs outputs open previews 1 open edit code dialog 1 write whatever python code you like 1 see the results in real time 1 be careful about infinite loops 1 in long loops use intpoint it will allow the code to be interrupted when it s needed 1 to store state of the code element you can use memory a dict which survives recalculations generating python code from the diagram 1 right click on last element of the diagram 1 select generate code the code will be copied to system clipboard 1 paste the code to empty python file 1 you can use the code as a library or as a script note code generation is experimental it may not work correctly with diagrams utilizing sequences or some sophisticated elements creating your own elements adding elements to cv lab is really simple see cvlab experimental sample py known issues random crashes due to a bug in old versions of opencv python binding 3 1 some opencv functions may cause random crashes of the entire application please use latest version of opencv available on the official opencv website https opencv org releases html alternatively you can install latest unofficial build of opencv using pip pip3 install upgrade opencv python note that most linux os packages often use outdated version of opencv before using above command you should uninstall them broken python generated code automatic code generation is experimental only experienced users shall use it some elements cannot be easily translated to python script code also code generated from diagrams utilizing sequences may not work correctly please forgive us copyright cv lab copyright c 2013 2019 adam brzeski jan cychnerski this software is distributed under agpl 3 0 license excluding cvlab diagram elements and cvlab thirdparty files in directory cvlab diagram elements are distributed under mit license files in directory cvlab thirdparty are distributed under their specific licenses | cv-lab opencv computer-vision python image-processing visual-programming prototyping | ai |
nodejs-polars | polars polars blazingly fast dataframes in rust python node js r and sql rust docs https docs rs polars badge svg https docs rs polars latest polars build and test https github com pola rs polars workflows build 20and 20test badge svg https github com pola rs polars actions https img shields io crates v polars svg https crates io crates polars pypi latest release https img shields io pypi v polars svg https pypi org project polars npm latest release https img shields io npm v nodejs polars svg https www npmjs com package nodejs polars usage importing js esm import pl from nodejs polars require const pl require nodejs polars series js const fooseries pl series foo 1 2 3 fooseries sum 6 a lot operations support both positional and named arguments you can see the full specs in the docs or the type definitions fooseries sort true fooseries sort reverse true shape 3 series foo f64 3 2 1 fooseries toarray 1 2 3 series are iterables so you can use javascript iterable syntax on them fooseries 1 2 3 fooseries 0 1 dataframe js const df pl dataframe a 1 2 3 4 5 fruits banana banana apple apple banana b 5 4 3 2 1 cars beetle audi beetle beetle beetle df sort fruits select fruits cars pl lit fruits alias literal string fruits pl col b filter pl col cars eq pl lit beetle sum pl col a filter pl col b gt 2 sum over cars alias sum a by cars pl col a sum over fruits alias sum a by fruits pl col a reverse over fruits flatten alias rev a by fruits shape 5 8 fruits cars literal stri b sum a by ca sum a by fr rev a by fr ng fruits rs uits uits str str i64 str i64 i64 i64 apple beetle fruits 11 4 7 4 apple beetle fruits 11 4 7 3 banana beetle fruits 11 4 8 5 banana audi fruits 11 2 8 2 banana beetle fruits 11 4 8 1 js df cars or df getcolumn cars shape 5 series cars str beetle beetle beetle audi beetle node setup install the latest polars version with sh yarn add nodejs polars yarn npm i s nodejs polars npm releases happen quite often weekly every few days at the moment so updating polars regularly to get the latest bugfixes features might not be a bad idea minimum requirements node version 18 rust version 1 59 only needed for development deno in deno modules you can import polars straight from npm typescript import pl from npm nodejs polars with deno 1 37 you can use the display function to display a dataframe in the notebook typescript import pl from npm nodejs polars import display from https deno land x display v1 1 1 mod ts let response await fetch https cdn jsdelivr net npm world atlas 1 world 110m tsv let data await response text let df pl readcsv data sep t await display df with deno 1 38 you only have to make the dataframe be the last expression in the cell typescript import pl from npm nodejs polars let response await fetch https cdn jsdelivr net npm world atlas 1 world 110m tsv let data await response text let df pl readcsv data sep t df img width 510 alt image src https github com pola rs nodejs polars assets 836375 90cf7bf4 7478 4919 b297 f8eb6a16196f documentation want to know about all the features polars supports read the docs python installation guide pip3 install polars python documentation https pola rs github io polars py polars html reference index html user guide https pola rs github io polars book rust rust documentation master branch https pola rs github io polars polars index html user guide https pola rs github io polars book node installation guide yarn install nodejs polars node documentation https pola rs github io nodejs polars user guide https pola rs github io polars book contribution want to contribute read our contribution guideline https github com pola rs polars blob master contributing md node compile polars from source if you want a bleeding edge release or maximal performance you should compile polars from source 1 install the latest rust compiler https www rust lang org tools install 2 run npm yarn install 3 choose any of fastest binary very long compile times bash cd nodejs polars yarn build yarn build ts this will generate a bin directory with the compiles ts code as well as the rust binary debugging fastest compile times but slow large binary bash cd nodejs polars yarn build debug yarn build ts this will generate a bin directory with the compiles ts code as well as the rust binary webpack configuration to use nodejs polars with webpack https webpack js org please use node loader https github com webpack contrib node loader and webpack config js acknowledgements development of polars is proudly powered by xomnia https raw githubusercontent com pola rs polars static master sponsors xomnia png https www xomnia com sponsors img src https raw githubusercontent com pola rs polars static master sponsors xomnia png height 40 https www xomnia com emsp img src https www jetbrains com company brand img jetbrains logo png height 50 https www jetbrains com | polars | front_end |
Go-full-stack | go full stack full stack app development with frontend angular and backend node express and mongo db technologies | server |
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ceit-management | heart college of engineering and information technology heart arrow forward hammer ceit management app by snoopycodex hammer arrow backward gem features gem heavy check mark manage students heavy check mark manage parents heavy check mark manage teachers heavy check mark manage classes key security key heavy check mark secure transferring of sensitive data heavy check mark api used is not prone to sqli heavy check mark api uses cloudflare s ssl certificate telephone receiver contact telephone receiver gmail https img shields io badge gmail 23ff0000 svg style for the badge logo gmail logocolor white mailto extremeclasherph gmail com facebook https img shields io badge facebook 231877f2 svg style for the badge logo facebook logocolor white https facebook com snoopycodex twitter https img shields io badge twitter 231da1f2 svg style for the badge logo twitter logocolor white https twitter com snoopycodex youtube https img shields io badge youtube 23ff0000 svg style for the badge logo youtube logocolor white https www youtube com channel ucc65iafgihvmci1vv i8osq | server |
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Udacity-Computer-Vision-Nanodegree | udacity computer vision nanodegree udacity computer vision nanodegree https tugan0329 bitbucket io imgs github cvnd svg style flat square https www udacity com course computer vision nanodegree nd891 github forks https img shields io github forks darkmatter18 udacity computer vision nanodegree style flat square https github com darkmatter18 udacity computer vision nanodegree network github stars https img shields io github stars darkmatter18 udacity computer vision nanodegree style flat square https github com darkmatter18 udacity computer vision nanodegree stargazers top language https img shields io github languages top darkmatter18 udacity computer vision nanodegree color orange style flat square logo jupyter https github com darkmatter18 udacity computer vision nanodegree lincence https img shields io github license darkmatter18 udacity computer vision nanodegree style flat square https github com darkmatter18 udacity computer vision nanodegree blob master license udacityfacebookscholar my valueable gems of computer vision nanodegree is stored in this repository p align center img src https raw githubusercontent com darkmatter18 udacity computer vision nanodegree master images nd banner2 jpg alt nanodegree banner p overview this nanodegree is the part secure and private ai scholarship chanllege sponsered by facebook ai after sortlisted from phase 1 me along with 299 others are in phase 2 persuing the computer vision nd or the deep learning nd content 1 cvnd exercises cvnd exercises all execises done under the nanodegree 2 p1 facial keypoint p1 facial keypoints files and notebooks of project 1 3 p2 image captioning p2 image captioning files and notebooks of project 2 4 mini project 2d histogram filter mini project two 20dimensional 20histogram 20filter mini project 2d histogram filter spaic banner https raw githubusercontent com darkmatter18 udacity computer vision nanodegree master images spaic p2 banner png maintainer relative date https img shields io date 1577392258 color important label started logo github https github com darkmatter18 maintenance https img shields io maintenance yes 2020 color green logo github https github com darkmatter18 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 0 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 0 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 1 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 1 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 2 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 2 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 3 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 3 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 4 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 4 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 5 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 5 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 6 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 6 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree images 7 https sourcerer io fame darkmatter18 darkmatter18 udacity computer vision nanodegree links 7 arkadip bhattacharya https www linkedin com in arkadip a href https twitter com arkadipb21 img src images twitter png width 32px height 32px a a href https www facebook com arkadipb img src images facebook png width 32px height 32px a a href https www linkedin com in arkadip img src images linkedin png width 32px height 32px a need help feel free to contact me in2arkadipb13 gmail com mailto in2arkadipb13 gmail com subject github udacity computer vision nanodegree repository github followers https img shields io github followers darkmatter18 color 1e88e5 label follow 20 40darkmatter18 logo github style flat square https github com darkmatter18 twitter follow https img shields io twitter follow arkadipb21 color 1e88e5 logo twitter style flat square https twitter com arkadipb21 honor code this repository created just to show off my works you are not eligible to copy any solution codes this repository shows respect and values the udacity honor code https www udacity com legal en us community guidelines | computer-vision udacity nanodegree computer-vision-nanodegree pytorch 2d-histogram-filter notebooks honor mini cnn lstm udacity-nanodegree udacity-computer-vision-nanodegree | ai |
cvt | cvt license https img shields io badge license bsd blue svg license cvt a computer vision toolkit cvt docs docs https yongyuan name cvt note div align left img width 20 alt a small reward is highly appreciated thank you src docs imgs weipayimg jpg div | covdet vlfeat image-matching sift hnsw cbir opq fasttext int8-quantization | ai |
StudyNotes | hometownm readme md hometownm hiarway readme md hiarway programminglanguagefundamentals studynotes https github com 100askteam studynotes tree main hiarway programminglanguagefundamentals studynotes arm7 arm9 arm11 arm cortex http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 arm7 arm9 arm11 arm cortex html arm arm risc cisc http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 arm arm risc cisc html arm m3 4 a7 http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 arm m34 a7 html arm http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 arm html bit http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 bit html cpu http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 cpu html flash http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 00 flash html rtc http studynotes 100ask net 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ba ef bc 88ring 20buffer ef bc 89 html arm bne beq http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 e6 b1 87 e7 bc 96 e8 af ad e8 a8 80 arm e6 b1 87 e7 bc 96 e4 b9 8b 20bne e4 b8 8ebeq html html http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 html dis http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 dis html c http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 c html http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 html html http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 html html http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 html b ldr http studynotes 100ask net hiarway programminglanguagefundamentals studynotes 02 b ldr html imx6ull baremetaldevelopment studynotes https github com 100askteam studynotes tree main hiarway imx6ull baremetaldevelopment studynotes html 00 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 00 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e5 b7 a5 e5 85 b7 e7 83 a7 e5 86 99 e6 95 b4 e4 b8 aa e7 b3 bb e7 bb 9f e6 88 96 e6 9b b4 e6 96 b0 e9 83 a8 e5 88 86 e7 b3 bb e7 bb 9f html 01 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 01 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e5 88 9d e5 a7 8b e7 8e af e5 a2 83 e6 90 ad e5 bb ba html 02 imx6ull imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 02 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9aimx6ull e5 90 af e5 8a a8 e6 b5 81 e7 a8 8b e8 a7 a3 e6 9e 90 html 03 imx6ull gpio http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 03 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e6 99 ae e9 80 82 e7 9a 84gpio e5 bc 95 e8 84 9a e6 93 8d e4 bd 9c e6 96 b9 e6 b3 95 e6 a6 82 e8 bf b0 html 04 imx6ull led http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 04 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88led e7 a8 8b e5 ba 8f ef bc 89 html 05 imx6ull led http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 04 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88led e7 a8 8b e5 ba 8f ef bc 89 html 06 imx6ull imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 06 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9aimx6ull e6 97 b6 e9 92 9f e4 bd 93 e7 b3 bb html 07 imx6ull cpu http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 07 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88 e6 94 b9 e5 8f 98cpu e5 b7 a5 e4 bd 9c e9 a2 91 e7 8e 87 ef bc 89 e5 a4 8d e4 bb b6 html 08 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 08 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88 e6 89 93 e5 8d b0 e6 97 b6 e9 92 9f e4 bf a1 e5 8f b7 e7 9a 84 e9 a2 91 e7 8e 87 e5 80 bc ef bc 89 html 09 imx6ull uart http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 09 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9auart e4 b8 b2 e5 8f a3 e7 ae 80 e4 bb 8b e5 8f 8a e5 af 84 e5 ad 98 e5 99 a8 e4 bb 8b e7 bb 8d html 10 imx6ull uart http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 10 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88uart e7 bc 96 e7 a8 8b ef bc 89 html 11 imx6ull printf http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 11 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88 e7 a7 bb e6 a4 8dprintf ef bc 89 html 12 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 12 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e4 bb a3 e7 a0 81 e9 87 8d e5 ae 9a e4 bd 8d html 13 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 13 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b ef bc 88 e4 bb a3 e7 a0 81 e9 87 8d e5 ae 9a e4 bd 8d e6 b5 8b e8 af 95 ef bc 89 html 14 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 14 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e5 bc 82 e5 b8 b8 e5 a4 84 e7 90 86 ef bc 88 e5 9f ba e7 a1 80 e6 a6 82 e5 bf b5 ef bc 89 html a href http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 15 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e7 a8 8b e5 ba 8f e7 a4 ba e4 be 8b e6 9c aa e5 ae 9a e4 b9 89 e6 8c 87 e4 bb a4 e5 bc 82 e5 b8 b8 e5 92 8csvc e5 bc 82 e5 b8 b8 ef bc 89 html 15 imx6ull svc a 16 imx6ull http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 16 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9a e4 b8 ad e6 96 ad e5 a4 84 e7 90 86 ef bc 88 e5 b1 9e e4 ba 8e e4 b8 80 e7 a7 8d e5 bc 82 e5 b8 b8 ef bc 89 html 17 imx6ull gpio http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 17 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9agpio e4 b8 ad e6 96 ad e7 bc 96 e7 a8 8b html 18 imx6ull i2c http studynotes 100ask net hiarway imx6ull baremetaldevelopment studynotes html e6 96 87 e4 bb b6 18 20imx6ull e8 a3 b8 e6 9c ba e5 bc 80 e5 8f 91 ef bc 9ai2c e5 8d 8f e8 ae ae html rtos training studynotes https github com 100askteam studynotes tree main hiarway rtos training studynotes 1 hal 1 01 c http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 01 c html 1 02 hal http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 02 hal html 1 03 at http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 03 at html 1 04 http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 04 html 1 05 http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 05 html 1 06 http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 06 html 1 07 http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 07 html 1 08 http studynotes 100ask net hiarway rtos training studynotes 01 hal 1 08 html 2 freertos 2 01freertos a http studynotes 100ask net hiarway rtos training studynotes 02 01freertos 2 01freertos a html 2 02freertos b http studynotes 100ask net hiarway rtos training studynotes 02 01freertos 2 02freertos b html 2 03 freertos http studynotes 100ask net hiarway rtos training studynotes 02 01freertos 2 03 freertos html 2 04freertos c http studynotes 100ask net hiarway rtos training studynotes 02 01freertos 2 04freertos c html freertos linux studynotes 01 imx6ull 100ask imx6ull pro md hometownm linux studynotes 01 imx6ull 100ask imx6ull pro md linux md hometownm linux studynotes 01 imx6ull linux md linux nano nano md hometownm linux studynotes 01 linux nano nano md zero zero md hometownm linux studynotes 01 linux zero zero md lcd md hometownm linux studynotes 01 03 lcd md iic md hometownm linux studynotes 01 04 iic md input md hometownm linux studynotes 01 05 input md pinctrl md hometownm linux studynotes 01 06 pinctrl md interrupt md hometownm linux studynotes 01 08 interrupt md github https github com 100askteam studynotes tree main hometownm linux studynotes 01 e9 a9 b1 e5 8a a8 e5 ad a6 e4 b9 a0 e6 b5 81 e7 a8 8b e5 9b be 02 linux md hometownm linux studynotes 02 linux md md hometownm linux studynotes 02 md md hometownm linux studynotes 02 md md hometownm linux studynotes 02 md rtos studynotes 01 4 freertos md hometownm rtos studynotes 01 4 freertos md 02 rtt 2021 12 11 pptx hometownm rtos studynotes 02 rtt 2021 12 11 pptx md hometownm rtos studynotes 03 md github https github com 100askteam studynotes tree main hometownm rtos studynotes e7 a8 8b e5 ba 8f e6 a1 86 e6 9e b6 e5 9b be | study linux rtos note book lerarning page | os |
AppStoreComparision | appstorecomparision android app store vs ios app store comparison advanced software engineering course | os |
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hybridos | hybridos hybridos is a totally new open source operating system designed for smart iot devices and cloud computing environment hybridos tries to provide the developers with more possibilities than just a traditional operating system for a stand alone hardware environment hybridos not only runs on smart iot devices to support application development on devices but also provides programming interfaces for the cloud and the clients it tries to give the developers a new complete software stack and protocol stack from devices to the cloud and the clients table of contents why we design a new os for iot why we design a new os for iot technical terminologies technical terminologies goals of hybridos goals of hybridos design documents and specifications design documents and specifications organization of source code organization of source code current status current status copying copying why we design a new os for iot at present when we want to develop a consumer iot internet of things device we have to build a software team which has five engineers at least due to the complex software stack and protocol stack of iot computing environment at least one firmware engineer who writes programs in c c for the iot device which generally runs a rtos or linux at least one server engineer who writes programs in java php python for the cloud services at least one front end engineer who writes webpages in javascript css html5 for webpages at least two client end engineers who write apps in java objective c or swift for smart phones which run android or ios in addition for some devices which are used for business we need one or two engineers to write desktop apps in c c for windows macos or linux obviously the development cost of an iot device is much higher than a traditional embedded device we need a new operating system to simplify the software and protocol stack of iot applications reduce the development cost and improve the productivity not only that the popular cloud computing services we can get from aws ali or huawei are not dedicated for iot applications we have to develop some cloud services by ourselves to implement some features for our iot devices moreover the security of an iot service is being seriously challenged for example with the development of iot technologies we can now use our own smart phone to remotely control the home air conditioner or view the home camera in real time we can even use the smart phone to remotely open our home door from the office however have you ever thought about the issue how your smart lock knows that the received door open command is sent by you or conversely is the real time monitoring screen that you see on your smart phone generated by your own camera the reality is that any cracker who breaks into the cloud can easily control all iot devices connected to the cloud remotely including your smart door locks or cameras the current iot products like traditional internet applications have not improved much in terms of security the scenes that we can often see from spy war movies can still be easily performed in reality this is because most iot developers are not security experts and they often downplay the security challenges of the iot system or choose a bad solution to implement a security policy some ideas from the blockchain technology may change this the decentralized design of the blockchain provides another way to enhance the security of iot hybridos will try to utilize some parts of blockchain technology and the mature and successful practice from the industry to improve the security of iot we see hybridos as an operating system running on the network we are committed to freeing iot developers from various protocol stacks and complex software stacks at the same time hybridos hides the security implementation details in order that the developers can focus on their applications technical terminologies device the iot device for hybridos the device here refers to a smart device that has direct access to the internet and with or without a display device app an app runs on a device with or without gui client a desktop computer a smart phone or a tablet client app an app runs on a client node the iot node for hybridos the node here refers to a constrained iot device in a constrained network goals of hybridos a new programming language for universal app hybridos provides a new and universal app framework for iot devices and client apps the developers can write device app and client apps for linux windows macos android and ios operating systems by using hvml hybrid virtual markup language hvml provides a data driven programming model and one developer can easily write an app with gui by using hvml like writing a html document hvml also provides a way to integrate external modules writing in other programming languages on low end iot devices you use c as the external programming language while you use python or javascript on high end iot devices and or clients as the external programming language finally the app programming languages of hybridos will be reduced to two hvml and one object oriented programming language python javascript or c no matter on device mobile phone computer or server as a result the development cost of an iot application will be reduced greatly by using hybridos a specialized software stack for iot hybridos provides a new software stack for iot applications a new implementation for common server such as data bus http coap and streaming servers hybridos provides a different architecture for the implementation of the servers any hybridos app or service can register and work as a real service provider or a request provider of the servers iot dedicated cloud computing services and security implementation hybridos integrates some cloud computing services which are dedicated to iot such as a distributed mqtt server identity authentication mechanism and some basic services such as firmware and app upgrade hybridos will introduce the serverless technology for the iot cloud computing in this way the developer can easily integrate the existed cloud services by writing a simple script in python in the future hybridos will try to provide a blockchain based iot security service hybridos will provide an enhanced mqtt implementation for communication between things and an identity authentication mechanism based on blockchain technology idea rich connectivity on the device side hybridos integrates a standard peripheral and task management interface based on hibus an enhanced edition of openwrt s ubus such as networking management sensor like gps and gravity accelerometer management and usb interface management hibus exchanges data among apps and services in json which is friendly for any programming language hybridos will provide a variety of connectivity options for the iot devices including 4g lte nb iot wi fi bluetooth zigbee nfc rfid usb ethernet rs232 and so on the device side system of hybridos is based on the linux kernel making full use of the linux kernel ecosystem reducing the difficulty of developing various drivers and the difficulty of supporting various protocol stacks thus reducing development cost design documents and specifications hybridos architecture gives you a whole picture of hybridos for all documents please refer to hybridos documents organization of source code main repository the main repository of hybridos is https github com fmsoftcn hybridos it mainly contains documents specifications and building scripts for hybridos the source code of hybridos is divided into three parts device side device side the part running on devices which use linux kernel server side server side the part running on servers in cloud client side client side the part running on an existing operating system such as windows gnu linux ios or android the building scripts and samples are located in the following directories for different sides device side the building scripts and samples for device side server side the building scripts and samples for server side client side the building scripts and samples for client side docs the design documents and specifications of hybridos other repositories we use some popular open source software as the common infrastructure of hybridos however we have done a lot of adjustments and optimizations for some of the key software we call the changed versions of the software as derivatives for hybridos and maintain them through the following public repositories app framework hiwebkit the hybridos derivative of webkit only tarball graphics stack himesa the hybridos derivative of mesa https github com fmsoftcn himesa hicairo the hybridos derivative of cairo https github com fmsoftcn hicairo hidrmdrivers drm drivers for hybridos https github com fmsoftcn hidrmdrivers minigui the window system for hybridos https github com vincentwei minigui system servers hihttp the http server for hybridos device side stay tuned hibus the data bus server for hybridos device side https github com fmsoftcn hibus system daemons hilogged the hybridos logging service stay tuned hisecd the hybridos security service stay tuned current status sep 2020 fmsoft announces the availability of the app running environment of hybridos for device side hishell aug 2020 fmsoft announces the availability of the key component of hybridos for device side hiwebkit mar 2020 fmsoft announces the availability of minigui 5 0 and the updated graphics stack of hybridos for device side nov 2019 initial release of hicairo nov 2018 initiate this project and organize specifications and design documents copying copyright c 2018 2020 beijing fmsoft technologies co ltd for device side and client side hybridos uses gplv3 or lgplv3 for server side hybridos uses agplv3 for documents gplv3 applies if a component of hybridos is a derivative of an existing open source software we generally continue to use the original license also note that hybridos integrates many mature open source software such as zlib libpng libjpeg sqlite freetype harfbuzz and so on for the copyright owners and licenses for these software please refer to the readme or license files contained in the source tarballs mesa https mesa3d org cairo https www cairographics org hybridos documents docs readme md hybridos architecture docs specs hybridos architecture md hybridos code and development convention docs specs hybridos code and development convention md beijing fmsoft technologies co ltd https www fmsoft cn fmsoft technologies https www fmsoft cn hybridos official site https hybridos fmsoft cn minigui http www minigui com webkit https webkit org html 5 3 https www w3 org tr html53 | os |
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TDengine | p p align center a href https tdengine com target blank img src docs assets tdengine svg alt tdengine width 500 a p p build status https cloud drone io api badges taosdata tdengine status svg ref refs heads master https cloud drone io taosdata tdengine build status https ci appveyor com api projects status kf3pwh2or5afsgl9 branch master svg true https ci appveyor com project sangshuduo tdengine 2n8ge branch master coverage status https coveralls io repos github taosdata tdengine badge svg branch develop https coveralls io github taosdata tdengine branch develop cii best practices https bestpractices coreinfrastructure org projects 4201 badge https bestpractices coreinfrastructure org projects 4201 br twitter follow https img shields io twitter follow tdenginedb label tdengine style social https twitter com tdenginedb youtube channel https img shields io badge subscribe tdengine white logo youtube style social https www youtube com tdengine discord community https img shields io badge join discord white logo discord style social https discord com invite vzdsuug4ps linkedin https img shields io badge follow linkedin white logo linkedin style social https www linkedin com company tdengine stackoverflow https img shields io badge ask stackoverflow white logo stackoverflow style social logocolor orange https stackoverflow com questions tagged tdengine english readme cn md tdengine cloud https cloud tdengine com learn more about tsdb https tdengine com tsdb what is tdengine tdengine is an open source high performance cloud native time series database https tdengine com tsdb optimized for internet of things iot connected cars and industrial iot it enables efficient real time data ingestion processing and monitoring of tb and even pb scale data per day generated by billions of sensors and data collectors tdengine differentiates itself from other time series databases with the following advantages high performance https tdengine com tdengine high performance time series database tdengine is the only time series database to solve the high cardinality issue to support billions of data collection points while out performing other time series databases for data ingestion querying and data compression simplified solution https tdengine com tdengine simplified time series data solution through built in caching stream processing and data subscription features tdengine provides a simplified solution for time series data processing it reduces system design complexity and operation costs significantly cloud native https tdengine com tdengine cloud native time series database through native distributed design sharding and partitioning separation of compute and storage raft support for kubernetes deployment and full observability tdengine is a cloud native time series database and can be deployed on public private or hybrid clouds ease of use https tdengine com tdengine easy time series data platform for administrators tdengine significantly reduces the effort to deploy and maintain for developers it provides a simple interface simplified solution and seamless integrations for third party tools for data users it gives easy data access easy data analytics https tdengine com tdengine time series data analytics made easy through super tables storage and compute separation data partitioning by time interval pre computation and other means tdengine makes it easy to explore format and get access to data in a highly efficient way open source https tdengine com tdengine open source time series database tdengine s core modules including cluster feature are all available under open source licenses it has gathered 19 9k stars on github there is an active developer community and over 139k running instances worldwide for a full list of tdengine competitive advantages please check here https tdengine com tdengine the easiest way to experience tdengine is through tdengine cloud https cloud tdengine com documentation for user manual system design and architecture please refer to tdengine documentation https docs tdengine com tdengine https docs taosdata com building at the moment tdengine server supports running on linux windows macos systems any application can also choose the restful interface provided by taosadapter to connect the taosd service tdengine supports x64 arm64 cpu and it will support mips64 alpha64 arm32 risc v and other cpu architectures in the future right now we don t support build with cross compiling environment you can choose to install through source code container https docs tdengine com get started docker installation package https docs tdengine com get started package or kubernetes https docs tdengine com deployment k8s this quick guide only applies to installing from source tdengine provide a few useful tools such as taosbenchmark was named taosdemo and taosdump they were part of tdengine by default tdengine compiling does not include taostools you can use cmake dbuild tools true to make them be compiled with tdengine to build tdengine use cmake https cmake org 3 0 2 or higher versions in the project directory install build tools ubuntu 18 04 and above or debian bash sudo apt get install y gcc cmake build essential git libssl dev libgflags2 2 libgflags dev install build dependencies for taostools to build the taostools https github com taosdata taos tools on ubuntu debian the following packages need to be installed bash sudo apt install build essential libjansson dev libsnappy dev liblzma dev libz dev zlib1g pkg config centos 7 9 bash sudo yum install epel release sudo yum update sudo yum install y gcc gcc c make cmake3 gflags git openssl devel sudo ln sf usr bin cmake3 usr bin cmake centos 8 fedora rocky linux bash sudo dnf install y gcc gcc c make cmake epel release gflags git openssl devel install build dependencies for taostools on centos centos 7 9 sudo yum install y zlib devel zlib static xz devel snappy devel jansson jansson devel pkgconfig libatomic libatomic static libstdc static openssl devel centos 8 fedora rocky linux sudo yum install y epel release sudo yum install y dnf plugins core sudo yum config manager set enabled powertools sudo yum install y zlib devel zlib static xz devel snappy devel jansson jansson devel pkgconfig libatomic libatomic static libstdc static openssl devel note since snappy lacks pkg config support refer to link https github com google snappy pull 86 it leads a cmake prompt libsnappy not found but snappy still works well if the powertools installation fails you can try to use sudo yum config manager set enabled powertools for centos devtoolset besides above dependencies please run following commands sudo yum install centos release scl sudo yum install devtoolset 9 devtoolset 9 libatomic devel scl enable devtoolset 9 bash macos brew install argp standalone gflags pkgconfig setup golang environment tdengine includes a few components like taosadapter developed by go language please refer to golang org official documentation for golang environment setup please use version 1 14 for the user in china we recommend using a proxy to accelerate package downloading go env w go111module on go env w goproxy https goproxy cn direct the default will not build taosadapter but you can use the following command to build taosadapter as the service for restful interface cmake dbuild http false setup rust environment tdengine includes a few components developed by rust language please refer to rust lang org official documentation for rust environment setup get the source codes first of all you may clone the source codes from github bash git clone https github com taosdata tdengine git cd tdengine you can modify the file gitconfig to use ssh protocol instead of https for better download speed you will need to upload ssh public key to github first please refer to github official documentation for detail url git github com insteadof https github com special note jdbc connector https github com taosdata taos connector jdbc go connector https github com taosdata driver go python connector https github com taosdata taos connector python node js connector https github com taosdata taos connector node c connector https github com taosdata taos connector dotnet rust connector https github com taosdata taos connector rust and grafana plugin https github com taosdata grafanaplugin has been moved to standalone repository build tdengine on linux platform you can run the bash script build sh to build both tdengine and taostools including taosbenchmark and taosdump as below bash build sh it equals to execute following commands bash mkdir debug cd debug cmake dbuild tools true dbuild contrib true make you can use jemalloc as memory allocator instead of glibc apt install autoconf cmake djemalloc enabled true tdengine build script can detect the host machine s architecture on x86 64 x86 arm64 platform you can also specify cputype option like aarch64 too if the detection result is not correct aarch64 bash cmake dcputype aarch64 cmake build on windows platform if you use the visual studio 2013 please open a command window by executing cmd exe please specify amd64 for 64 bits windows or specify x86 for 32 bits windows when you execute vcvarsall bat cmd mkdir debug cd debug c program files x86 microsoft visual studio 12 0 vc vcvarsall bat amd64 x86 cmake g nmake makefiles nmake if you use the visual studio 2019 or 2017 please open a command window by executing cmd exe please specify x64 for 64 bits windows or specify x86 for 32 bits windows when you execute vcvarsall bat cmd mkdir debug cd debug c program files x86 microsoft visual studio 2019 community vc auxiliary build vcvarsall bat x64 x86 cmake g nmake makefiles nmake or you can simply open a command window by clicking windows start visual studio 2019 2017 folder x64 native tools command prompt for vs 2019 2017 or x86 native tools command prompt for vs 2019 2017 depends what architecture your windows is then execute commands as follows cmd mkdir debug cd debug cmake g nmake makefiles nmake on macos platform please install xcode command line tools and cmake verified with xcode 11 4 on catalina and big sur shell mkdir debug cd debug cmake cmake build installing on linux platform after building successfully tdengine can be installed by bash sudo make install users can find more information about directories installed on the system in the directory and files https docs tdengine com reference directory section installing from source code will also configure service management for tdengine users can also choose to install from packages https docs tdengine com get started package for it to start the service after installation in a terminal use bash sudo systemctl start taosd then users can use the tdengine cli to connect the tdengine server in a terminal use bash taos if tdengine cli connects the server successfully welcome messages and version info are printed otherwise an error message is shown on windows platform after building successfully tdengine can be installed by cmd nmake install on macos platform after building successfully tdengine can be installed by bash sudo make install users can find more information about directories installed on the system in the directory and files https docs tdengine com reference directory section installing from source code will also configure service management for tdengine users can also choose to install from packages https docs tdengine com get started package for it to start the service after installation double click the applications tdengine to start the program or in a terminal use bash sudo launchctl start com tdengine taosd then users can use the tdengine cli to connect the tdengine server in a terminal use bash taos if tdengine cli connects the server successfully welcome messages and version info are printed otherwise an error message is shown quick run if you don t want to run tdengine as a service you can run it in current shell for example to quickly start a tdengine server after building run the command below in terminal we take linux as an example command on windows will be taosd exe bash build bin taosd c test cfg in another terminal use the tdengine cli to connect the server bash build bin taos c test cfg option c test cfg specifies the system configuration file directory try tdengine it is easy to run sql commands from tdengine cli which is the same as other sql databases sql create database demo use demo create table t ts timestamp speed int insert into t values 2019 07 15 00 00 00 10 insert into t values 2019 07 15 01 00 00 20 select from t ts speed 19 07 15 00 00 00 000 10 19 07 15 01 00 00 000 20 query ok 2 row s in set 0 001700s developing with tdengine official connectors tdengine provides abundant developing tools for users to develop on tdengine follow the links below to find your desired connectors and relevant documentation java https docs tdengine com reference connector java c c https docs tdengine com reference connector cpp python https docs tdengine com reference connector python go https docs tdengine com reference connector go node js https docs tdengine com reference connector node rust https docs tdengine com reference connector rust c https docs tdengine com reference connector csharp restful api https docs tdengine com reference rest api contribute to tdengine please follow the contribution guidelines contributing md to contribute to the project join the tdengine community for more information about tdengine you can follow us on social media and join our discord server discord https discord com invite vzdsuug4ps twitter https twitter com tdenginedb linkedin https www linkedin com company tdengine youtube https www youtube com tdengine | iot bigdata time-series database industrial-iot connected-vehicles monitoring tsdb tdengine sql time-series-database scalability cluster metrics financial-analysis cloud-native distributed | server |
joeferrara.github.io | joeferrara github io ios xcuitest and test engineering blog | os |
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llm-hub | large language models showcase welcome to the large language models showcase this repository is a curated collection of interesting applications use cases github repos and tutorials that use state of the art language models such as gpt 3 and other large language models whether you re a language enthusiast a machine learning researcher or just someone interested in the capabilities of ai this repository is the perfect place to explore the world of natural language processing and see what these powerful models are capable of in this repository you ll find a variety of examples and demonstrations of language models being used for text generation search question answering and more you ll also find tutorials and resources for building your own applications as well as links to other repositories and resources for further learning showcases june 6 2023 babyagi github https github com yoheinakajima babyagi this python script is an example of an ai powered task management system the system uses openai and vector databases such as chroma or weaviate to create prioritize and execute tasks the main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective the script then uses openai s natural language processing nlp capabilities to create new tasks based on the objective and chroma weaviate to store and retrieve task results for context this is a pared down version of the original task driven autonomous agent mar 28 2023 remo github https github com daveshap remo framework rolling episodic memory organizer remo for autonomous ai systemsremo recursive episodic memory organizer efficient scalable memory management organizes conversational data into taxonomical ranks each rank clusters semantically similar elements powerful tool for context aware ai systems improves conversational capabilities recall accuracy window ai github https github com alexanderatallah window ai window ai is a browser extension that lets you configure ai models in one place and use them on the web for developers easily make multi model apps free from api costs and limits just use the injected window ai library leverage decentralized ai for users control the ai you use on the web whether it s external like openai proxied or local to protect privacy for model providers plug into an ecosystem of users without requiring developers to change their apps openchatkit github https github com togethercomputer openchatkit openchatkit provides a powerful open source base to create both specialized and general purpose chatbots for various applications the kit includes an instruction tuned language models a moderation model and an extensible retrieval system for including up to date responses from custom repositories alpaca turbo github https github com viperx7 alpaca turbo alpaca turbo is a frontend to use large language models that can be run locally without much setup required it is a user friendly web ui for the llama cpp with unique features that make it stand out from other implementations the goal is to provide a seamless chat experience that is easy to configure and use without sacrificing speed or functionality motorhead github https github com getmetal motorhead when building chat applications using llms memory handling is something that has to be built every time motorhead is a server to assist with that process gptcache github https github com zilliztech gptcache chatgpt and various large language models llms boast incredible versatility enabling the development of a wide range of applications however as your application grows in popularity and encounters higher traffic levels the expenses related to llm api calls can become substantial additionally llm services might exhibit slow response times especially when dealing with a significant number of requests to tackle this challenge we have created gptcache a project dedicated to building a semantic cache for storing llm responses babyagi asi github https github com oliveirabruno01 babyagi asi this python script is an example of a llm powered autonomous agent the system uses openai api to create and execute tasks the core idea of the project is to provide the assistant with the tools it needs to do any task if it s smart enough it can arbitrarily execute code and control its own flow and memory for a sufficiently intelligent agent either by pre training fine tuning or prompt optimization this should be enough if it is possible at all eval github https github com corca ai eval eval elastic versatile agent with langchain will execute all your requests just like an eval method building llm applications for production blog https huyenchip com 2023 04 11 llm engineering html a question that i ve been asked a lot recently is how large language models llms will change machine learning workflows after working with several companies who are working with llm applications and personally going down a rabbit hole building my applications chameleon github https github com lupantech chameleon llm chameleon is a plug and play compositional reasoning framework that augments llms with various types of tools chameleon synthesizes programs to compose various tools including llm models off the shelf vision models web search engines python functions and rule based modules tailored to user interests built on top of an llm as a natural language planner chameleon infers the appropriate sequence of tools to compose and execute in order to generate a final response fastchat github https github com lm sys fastchat an open platform for training serving and evaluating large language models release repo for vicuna and fastchat t5 vicuna an open source chatbot impressing gpt 4 with 90 chatgpt quality github https lmsys org blog 2023 03 30 vicuna we introduce vicuna 13b an open source chatbot trained by fine tuning llama on user shared conversations collected from sharegpt preliminary evaluation using gpt 4 as a judge shows vicuna 13b achieves more than 90 quality of openai chatgpt and google bard while outperforming other models like llama and stanford alpaca in more than 90 of cases mr ranedeer your personalized ai tutor github https github com jushbjj mr ranedeer ai tutor unlock the potential of gpt 4 with mr ranedeer ai tutor a customizable prompt that delivers personalized learning experiences for users with diverse needs and interests releasing 3b and 7b redpajama incite family of models blog https www together xyz blog redpajama models v1 the redpajama project aims to create a set of leading open source models and to rigorously understand the ingredients that yield good performance openlm research github https github com openlm research open llama we are releasing our public preview of openllama a permissively licensed open source reproduction of meta ai s llama 7b trained on the redpajama dataset our model weights can serve as the drop in replacement of llama 7b in existing implementations we also provide a smaller 3b variant of llama model knowledge github https github com knowledgecanvas knowledge dive into a more interactive learning experience with knowledge s new chat feature engage in dynamic conversations with your projects and sources leveraging the power of large language models ask questions explore concepts and deepen your understanding all within an intuitive chat interface mlc llm github https github com mlc ai mlc llm mlc llm is a universal solution that allows any language models to be deployed natively on a diverse set of hardware backends and native applications plus a productive framework for everyone to further optimize model performance for their own use cases amazing bard prompts github https github com dsdanielpark amazing bard prompts the interactive artificial intelligence google bard released by google ai officially supports english korean and japanese therefore we share prompts for better use of google bard amazing bard prompts is a fork of awesome chatgpt prompts and translated with google s translate engine contributors can modify csv files to edit review and suggest prompts that are suitable for google bard openai cookbook github https github com openai openai cookbook the openai cookbook shares example code for accomplishing common tasks with the openai api introduction to large language models for generative ai github https www assemblyai com blog introduction large language models generative ai generative ai language models like chatgpt are changing the way humans and ai interact and work together but how do these models actually work learn everything you need to know about modern generative ai for language in this simple guide superagent github https github com homanp superagent superagent is a powerful tool that simplifies the configuration and deployment of llm large language model agents to production it provides a range of features and functionalities to make it easier for developers to build manage and deploy ai agents to production including features such as built in memory and document retrieval via vector dbs powerful tools webhooks cron jobs etc e2b github https github com e2b dev e2b developer first agentops platform deploy test and monitor ai agents zeno build github https github com zeno ml zeno build zeno build is a tool for developers who want to quickly build compare and iterate on applications using large language models chatall github https github com sunner chatall concurrently chat with chatgpt bing chat bard alpaca vincuna claude chatglm moss iflytek spark ernie and more discover the best answers privategpt app github https github com menloparklab privategpt app a fastapi backend and a streamlit ui for privategpt interact privately with your documents as a webapp using the power of gpt 100 privately no data leaks llm numbers github https github com ray project llm numbers at google there was a document put together by jeff dean the legendary engineer called numbers every engineer should know it s really useful to have a similar set of numbers for llm developers to know that are useful for back of the envelope calculations here we share particular numbers we at anyscale use why the number is important and how to use it to your advantage generative ai for document understanding with hugging face and amazon sagemaker github https www philschmid de sagemaker donut in this tutorial you will learn how to fine tune and deploy donut base for document understand document parsing using hugging face transformers and amazon sagemaker pandagpt one model to instruction follow them all website https panda gpt github io pandagpt is a general purpose instruction following model that can both see and hear our pilot experiments show that pandagpt can perform complex tasks such as detailed image description generation writing stories inspired by videos and answering questions about audios april may 2023 langchain github https github com hwchase17 langchain building applications with llms through composability chatgpt memory github https github com continuum llms chatgpt memory allows to scale the chatgpt api to multiple simultaneous sessions with infinite contextual and adaptive memory powered by gpt and redis datastore awesome chatgpt prompts github https github com f awesome chatgpt prompts this is a collection of prompt examples to be used with the chatgpt model jarvis girhub https github com microsoft jarvis jarvis a system to connect llms with ml community marvin github https github com prefecthq marvin a batteries included library for building ai powered software marvin s job is to integrate ai directly into your codebase by making it look and feel like any other function llamaindex gpt index github https github com jerryjliu llama index llamaindex gpt index is a project that provides a central interface to connect your llm s with external data ontogpt github https github com monarch initiative ontogpt generation of ontologies and knowledge bases using gpt a knowledge extraction tool that uses a large language model to extract semantic information from text building a multi user chatbot with langchain and pinecone in next js blog https www pinecone io learn javascript chatbot utm content 244434553 utm medium social utm source twitter hss channel tw 1287624141001109504 building a chatbot has become a hot skill and with the release of chatgpt we see a huge number of chat applications being released efficient large language model training blog https www philschmid de fine tune flan t5 peft efficient large language model training with lora and hugging face chatdoctor a medical chat model fine tuned on llama model using medical domain knowledge github https github com kent0n li chatdoctor chatdoctor a medical chat model fine tuned on llama model using medical domain knowledge gpt4all github https github com nomic ai gpt4all gpt4all an ecosystem of open source chatbots trained on a massive collections of clean assistant data including code stories and dialogue demo data and code to train an assistant style large language model with 800k gpt 3 5 turbo generations based on llama dataless knowledge fusion by merging weights of language models github https github com bloomberg dataless model merging this repository contains the experimental code to reproduce the results in dataless knowledge fusion by merging weights of language models a paper to be published during the eleventh international conference on learning representations iclr 2023 to be held may 1 5 2023 in kigali rwanda prompt engineering guide github https github com dair ai prompt engineering guide guides papers lecture notebooks and resources for prompt engineering kor website https eyurtsev github io kor this is a half baked prototype that helps you extract structured data from text using large language models llms memory assisted prompt editing to improve gpt 3 after deployment github https github com madaan memprompt a method to fix gpt 3 after deployment with user feedback without re training chatgpt llms vs knowledge graphs blog https thecaglereport com 2023 03 24 chatgpt llms vs knowledge graphs this article focuses more on how chatgpt works at a conceptual level and how it compares to knowledge graphs gpt 4 langchain create a chatgpt chatbot for your pdf files github https github com pineappleexpress808 gpt4 pdf chatbot langchain use the new gpt 4 api to build a chatgpt chatbot for multiple large pdf files leveraging langchain and large language models for accurate pdf based question answering github https github com mallahyari drqa this repo is to help you build a powerful question answering system that can accurately answer questions by combining langchain and large language models llms including openai s gpt3 models colossalchat github https github com hpcaitech colossalai tree main applications chat colossalchat is the project to implement llm with rlhf powered by the colossal ai project coati stands for colossalai talking intelligence it is the name for the module implemented in this project and is also the name of the large language model developed by the colossalchat project youtube semantic search github https github com transitive bullshit yt semantic search openai powered semantic search for any youtube playlist featuring the all in podcast sketch github https github com approximatelabs sketch sketch is an ai code writing assistant for pandas users that understands the context of your data greatly improving the relevance of suggestions sketch is usable in seconds and doesn t require adding a plugin to your ide vicuna an open source chatbot impressing gpt 4 with 90 chatgpt quality website https vicuna lmsys org we introduce vicuna 13b an open source chatbot trained by fine tuning llama on user shared conversations collected from sharegpt preliminary evaluation using gpt 4 as a judge shows vicuna 13b achieves more than 90 quality of openai chatgpt and google bard while outperforming other models like llama and stanford alpaca in more than 90 of cases agents in haystack make llms resolve complex tasks website https haystack deepset ai blog introducing haystack agents utm content 243448908 utm medium social utm source linkedin hss channel lcp 18582479 introducing the agent to the haystack ecosystem the implementation of agents is inspired by two papers the mrkl systems paper pronounced miracle and the react paper if you like reading papers i highly recommend these two here i ll explain how we re introducing this functionality to haystack fastchat girhub https github com lm sys fastchat an open platform for training serving and evaluating large language model based chatbots lmql website https lmql ai lmql is a programming language for language model interaction next js openai doc search starter girhub https github com supabase community nextjs openai doc search og v2 template for building your own custom chatgpt style doc search powered by next js openai and supabase python bindings for llama cpp github https github com abetlen llama cpp python simple python bindings for llama cpp library stackllama a hands on guide to train llama with rlhf website https huggingface co blog stackllama models such as chatgpt gpt 4 and claude are powerful language models that have been fine tuned using a method called reinforcement learning from human feedback rlhf to be better aligned with how we expect them to behave and would like to use them codesquire website https codesquire ai ai code writing assistant for data scientists engineers and analysts get code completions and suggestions as you type vscode chatgpt github https chatgptai dev a visual studio code chatgpt integration gptcache github https github com zilliztech gptcache the github repository gptcache is a high performance cache system for large scale language models like gpt it aims to reduce the inference latency and cost of gpt models by caching intermediate results semantic search using llamaindex and langchain blog https blog futuresmart ai semantic search using llamaindex and langchain the blog post discusses the implementation of semantic search using llamaindex and langchain llamaindex is a fast and memory efficient indexing system while langchain is a language model that can convert queries into embeddings for semantic search auto gpt an autonomous gpt 4 experiment github https github com torantulino auto gpt auto gpt is an experimental open source application showcasing the capabilities of the gpt 4 language model this program driven by gpt 4 autonomously develops and manages businesses to increase net worth as one of the first examples of gpt 4 running fully autonomously auto gpt pushes the boundaries of what is possible with ai accelerating llama with fabric a comprehensive guide to training and fine tuning llama website https lightning ai pages community tutorial accelerating llama with fabric a comprehensive guide to training and fine tuning llama in this tutorial we will learn how to train and fine tune llama large language model meta ai lit llama a rewrite of llama can run inference on an 8 gb consumer gpu we will also discover how it utilizes lightning fabric to accelerate the pytorch code tabby github https github com tabbyml tabby it provides a user friendly interface for interactive data analysis and exploration it allows users to easily filter sort and visualize data and also includes functionality for machine learning and natural language processing openplayground github https github com nat openplayground the github repository openplayground provides an interactive playground for exploring machine learning algorithms and neural networks it includes pre built models and datasets as well as the ability to upload custom data and models open llm leaderboard from hugging face website https huggingface co spaces huggingfaceh4 open llm leaderboard with the plethora of large language models llms and chatbots being released week upon week often with grandiose claims of their performance it can be hard to filter out the genuine progress that is being made by the open source community and which model is the current state of the art the open llm leaderboard aims to track rank and evaluate llms and chatbots as they are released a key advantage of this leaderboard is that anyone from the community can submit a model for automated evaluation on the gpu cluster as long as it is a transformers model with weights on the hub evaluation of models with delta weights for non commercial licensed models are also supported such as llama contribution contributions to this repository are welcome and encouraged if you have any interesting applications or tutorials that use language models please feel free to submit a pull request let s showcase the power of ai language models together license this repository is licensed under the mit license license | llms chatgpt gpt-3 gpt-4 deep-learning language-model nlp openai | ai |
pict_rtos_ws | pict rtos ws this repository is created for the pict rtos workshop please fork this repository and push solutions of any assignments into your own fork presentations some of the presentations shown in the workshops can be found here introduction to rtos https slides com chhajedji rtos intro introduction to bluetooth https slides com chhajedji bt intro provisioning apps esp softap provisioning https play google com store apps details id com espressif provsoftap hl en in gl us esp ble provisioning https play google com store apps details id com espressif provble hl en ie the list of tasks for git 1 to create a branch with your own name e g assignments aditya in the master branch 2 create a sub branch in it 3 raise a merge request for that sub branch select the assignee source branch target branch and merge the branch 4 learn how to resolve conflicts by making changes in the same file simultaneously raising a conflict 5 learn about interactive rebase and its applications 6 learn how to reset the commits and get back to a previous place 7 learn how to restore previous version of you file in case you lost it or you made wrong changes 8 learn commands like cherry pick diff 9 learn how to make patches send patches to friends and how to apply patches to the data 10 learn about detached head state and how to operate from there 11 learn how to move files from different origins like github to gitlab or viceversa 12 what are sub modules and what are they used for freertos task 1 producer consumer i create a task which allocates space from heap ii create a task which can free the space allocated by task in step 1 iii print available heap space in both the tasks | os |
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aiq-design-system | p align center a href https aiqfome com rel noopener target blank img width 150 src https www suafranquia com views sources images franquias logos 271b399b0a004c781779ec805e8d7ab7 png alt aiqfome logo a p p h1 align center aiq design system h1 div align center a biblioteca de componentes para projetos react da galera do aiqfome http www aiqfome com p align center a aria label contributors graph href https github com aiqfome aiq design system graphs contributors img src https img shields io github contributors aiqfome aiq design system svg a img alt language grade javascript src https img shields io lgtm grade javascript github aiqfome aiq design system svg logo lgtm logowidth 18 a aria label license href https github com aiqfome aiq design system blob master license img src https img shields io github license aiqfome aiq design system svg alt a img alt npm downloads src https img shields io npm dm aiq design system svg style flat npm downloads https img shields io npm dm aiq design system svg https www npmjs com package aiq design system p div como adicionar ao projeto sh yarn add aiq design system or npm install aiq design system os componentes nossos componentes est o nesse storybook https www chromatic com library appid 621085ba70b9d2003a142b7d documenta o oficial coming soon configura o b sica pra come ar a utilizar os componentes segue esses passos 1 a gente disponibiliza um wrapper pra voc adicionar na aplica o o themeprovider importado da aiq design system jsx import react from react import reactdom from react dom import themeprovider from aiq design system function app children return themeprovider children themeprovider 2 j pode sair utilizando os componentes importados da aiq design system jsx import react from react import reactdom from react dom import button flex input from aiq design system export const aiqcomponent return flex p 20 flexdirection column input variant outlined label duas pizzas muito button variant contained fazer pedido button flex template no codesandbox temos um template configurado no codesandbox com a lib configurada edit button https codesandbox io static img play codesandbox svg https codesandbox io s aiq design system yukfc | hacktoberfest react ui-components design-system components hacktoberfest2022 | os |
awesome-machine-learning-on-source-code | awesome machine learning on source code awesome machine learning on source code badges awesome svg https github com src d awesome machine learning on source code ci status https travis ci org src d awesome machine learning on source code svg https travis ci org src d awesome machine learning on source code awesome machine learning on source code img awesome machine learning artwork png notice this repository is no longer actively maintained and no further updates will be done nor issues prs will be answered or attended an alternative actively maintained can be found at ml4code github io https ml4code github io papers html repository https github com ml4code ml4code github io a curated list of awesome research papers datasets and software projects devoted to machine learning and source code mloncode https twitter com hashtag mloncode contents digests digests conferences conferences competitions competitions papers papers program synthesis and induction program synthesis and induction source code analysis and language modeling source code analysis and language modeling neural network architectures and algorithms neural network architectures and algorithms embeddings in software engineering embeddings in software engineering program translation program translation code suggestion and completion code suggestion and completion program repair and bug detection program repair and bug detection apis and code mining apis and code mining code optimization code optimization topic modeling topic modeling sentiment analysis sentiment analysis code summarization code summarization clone detection clone detection differentiable interpreters differentiable interpreters related research related research details summary links require related research spoiler to be open summary ast differencing ast differencing binary data modeling binary data modeling soft clustering using t mixture models soft clustering using t mixture models natural language parsing and comprehension natural language parsing and comprehension details posts posts talks talks software software machine learning machine learning utilities utilities datasets datasets credits credits contributions contributions license license digests learning from big code http learnbigcode github io techniques challenges tools datasets on big code a survey of machine learning for big code and naturalness https ml4code github io survey and literature review on machine learning on source code conferences img src badges origin academia blue svg alt origin academia align top acm international conference on software engineering icse https www icse2018 org img src badges origin academia blue svg alt origin academia align top acm international conference on automated software engineering ase https 2019 aseconf org img src badges origin academia blue svg alt origin academia align top acm joint european software engineering conference and symposium on the foundations of software engineering fse https conf researchr org home fse 2018 img src badges origin academia blue svg alt origin academia align top 2018 ieee 25th international conference on software analysis evolution and reengineering saner https www conference publishing com list php event saner18main img src badges origin academia blue svg alt origin academia align top machine learning for programming https ml4p org img src badges origin academia blue svg alt origin academia align top workshop on nlp for software engineering https nl4se github io img src badges origin industry green svg alt origin industry align top sysml http www sysml cc talks https www youtube com channel uchutdkia ayyambt45s991g img src badges origin academia blue svg alt origin academia align top mining software repositories http www msrconf org img src badges origin industry green svg alt origin industry align top aiforse https aiforse org img src badges origin industry green svg alt origin industry align top source d tech talks https blog sourced tech post ml talks moscow talks https www youtube com playlist list pl5ld68ole7j3iqfusb3fr9122dhcuwxsy img src badges origin academia blue svg alt origin academia align top nips neural abstract machines and program induction workshop https uclmr github io nampi talks https www youtube com playlist list plztdea cm27lvpstdk9rypsyqbhzwpywt img src badges origin academia blue svg alt origin academia align top camaiml https www microsoft com en us research event artificial intelligence and machine learning in cambridge 2017 learning to code machine learning for program induction https www youtube com watch v vzduvhfmb9q alexander gaunt img src badges origin academia blue svg alt origin academia align top mases 2018 https mases18 github io competitions codrep https github com kth codrep competition competition on automatic program repair given a source line find the insertion point papers program synthesis and induction img src badges 12 pages gray svg alt 12 pages align top program synthesis and semantic parsing with learned code idioms https arxiv org abs 1906 10816v2 richard shin miltiadis allamanis marc brockschmidt oleksandr polozov 2019 img src badges 16 pages gray svg alt 16 pages align top synthetic datasets for neural program synthesis https openreview net forum id ryeosnaqym richard shin neel kant kavi gupta chris bender brandon trabucco rishabh singh dawn song iclr 2019 img src badges 15 pages gray svg alt 15 pages align top execution guided neural program synthesis https openreview net forum id h1gfoiaqym xinyun chen chang liu dawn song iclr 2019 img src badges 8 pages gray svg alt 8 pages align top deepfuzz automatic generation of syntax valid c programs for fuzz testing https faculty ist psu edu wu papers deepfuzz pdf xiao liu xiaoting li rupesh prajapati dinghao wu aaai 2019 img src badges 12 pages beginner brightgreen svg alt 12 pages beginner align top nl2bash a corpus and semantic parser for natural language interface to the linux operating system https arxiv org abs 1802 08979v2 xi victoria lin chenglong wang luke zettlemoyer michael d ernst lrec 2018 img src badges 18 pages gray svg alt 18 pages align top recent advances in neural program synthesis https arxiv org abs 1802 02353v1 neel kant 2018 img src badges 16 pages gray svg alt 16 pages align top neural sketch learning for conditional program generation https arxiv org abs 1703 05698 vijayaraghavan murali letao qi swarat chaudhuri chris jermaine iclr 2018 img src badges 11 pages gray svg alt 11 pages align top neural program search solving programming tasks from description and examples https arxiv org abs 1802 04335v1 illia polosukhin alexander skidanov iclr 2018 img src badges 16 pages gray svg alt 16 pages align top neural program synthesis with priority queue training https arxiv org abs 1801 03526v1 daniel a abolafia mohammad norouzi quoc v le 2018 img src badges 31 pages gray svg alt 31 pages align top towards synthesizing complex programs from 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coen de roover ssbse 2016 img src badges 14 pages gray svg alt 14 pages align top structured generative models of natural source code https arxiv org abs 1401 0514 chris j maddison daniel tarlow icml 2014 source code analysis and language modeling img src badges 12 pages gray svg alt 12 pages align top modeling vocabulary for big code machine learning https arxiv org abs 1904 01873v1 hlib babii andrea janes romain robbes 2019 img src badges 24 pages gray svg alt 24 pages align top generative code modeling with graphs https openreview net forum id bke4ksa5fx marc brockschmidt miltiadis allamanis alexander l gaunt oleksandr polozov iclr 2019 img src badges 12 pages gray svg alt 12 pages align top nl2type inferring javascript function types from natural language information http software lab org publications icse2019 nl2type pdf rabee sohail malik jibesh patra michael pradel icse 2019 img src badges 12 pages gray svg alt 12 pages align top a novel neural source code representation based on abstract syntax tree http xuwang tech paper astnn icse2019 pdf jian zhang xu wang hongyu zhang hailong sun kaixuan wang xudong liu icse 2019 img src badges 11 pages gray svg alt 11 pages align top deep learning type inference http vhellendoorn github io pdf fse2018 j2t pdf vincent j hellendoorn christian bird earl t barr and miltiadis allamanis fse 2018 code https github com deeptyper deeptyper img src badges 12 pages gray svg alt 12 pages align top tree2tree neural translation model for learning source code changes https arxiv org pdf 1810 00314 pdf saikat chakraborty miltiadis allamanis baishakhi ray 2018 img src badges 11 pages gray svg alt 11 pages align top code2seq generating sequences from structured representations of code https arxiv org abs 1808 01400 uri alon omer levy eran yahav 2018 img src badges 12 pages gray svg alt 12 pages align top syntax and sensibility using language models to detect and correct syntax errors http softwareprocess es pubs santos2018saner syntax 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towardsdatascience com semantic code search 3cd6d244a39c learning from source code https www microsoft com en us research blog learning source code training a model to summarize github issues https towardsdatascience com how to create data products that are magical using sequence to sequence models 703f86a231f8 sequence intent classification using hierarchical attention networks https www microsoft com developerblog 2018 03 06 sequence intent classification syntax directed variational autoencoder for structured data https mlatgt blog 2018 02 08 syntax directed variational autoencoder for structured data weighted minhash on gpu helps to find duplicate github repositories https blog sourced tech post minhashcuda source code identifier embeddings https blog sourced tech post id2vec using recurrent neural networks to predict next tokens in the java solutions https codeforces com blog entry 52327 the half life of code the ship of theseus https erikbern com 2016 12 05 the half life of code html the eigenvector of why we moved from language x to language y https erikbern com 2017 03 15 the eigenvector of why we moved from language x to language y html analyzing github how developers change programming languages over time https blog sourced tech post language migrations topic modeling of github repositories https blog sourced tech post github topic modeling aroma using machine learning for code recommendation https ai facebook com blog aroma ml for code recommendation talks machine learning on source code http vmarkovtsev github io pydays 2018 vienna similarity of github repositories by source code identifiers http vmarkovtsev github io techtalks 2017 moscow using deep rnn to model source code http vmarkovtsev github io re work 2016 london source code abstracts classification using cnn 1 http vmarkovtsev github io re work 2016 berlin source code abstracts classification using cnn 2 http vmarkovtsev github io data natives 2016 source code abstracts classification using cnn 3 http vmarkovtsev github io slush 2016 embedding the github contribution graph https egorbu github io techtalks 2017 moscow measuring code sentiment in a git repository http vmarkovtsev github io gophercon 2018 moscow software machine learning differentiable neural computer dnc https github com deepmind dnc tensorflow implementation of the differentiable neural computer sourced ml https github com src d ml abstracts feature extraction from source code syntax trees and working with ml models vecino https github com src d vecino finds similar git repositories apollo https github com src d apollo source code deduplication as scale research gemini https github com src d gemini source code deduplication as scale production enry https github com src d enry insanely fast file based programming language detector hercules https github com src d hercules git repository mining framework with batteries on top of go git deepcs https github com guxd deep code search keras and pytorch implementations of deepcs deep code search code neuron https github com vmarkovtsev codeneuron recurrent neural network to detect code blocks in natural language text naturalize https github com mast group naturalize language agnostic framework for learning coding conventions from a codebase and then expoiting this information for suggesting better identifier names and formatting changes in the code extreme source code summarization https github com mast group convolutional attention convolutional attention neural network that learns to summarize source code into a short method name like summary by just looking at the source code tokens summarizing source code using a neural attention model https github com sriniiyer codenn code nn uses lstm networks with attention to produce sentences that describe c code snippets and sql queries from stackoverflow torch over c sql probabilistic api miner https github com mast group api mining near parameter free probabilistic algorithm for mining the most interesting api patterns from a list of api call sequences interesting sequence miner https github com mast group sequence mining novel algorithm that mines the most interesting sequences under a probabilistic model it is able to efficiently infer interesting sequences directly from the database tassal https github com mast group tassal tool for the automatic summarization of source code using autofolding autofolding automatically creates a summary of a source code file by folding non essential code and comment blocks jnice2predict http www nice2predict org efficient and scalable open source framework for structured prediction enabling one to build new statistical engines more quickly clone digger http clonedigger sourceforge net download html clone detection for python and java sensibility https github com naturalness sensibility uses lstms to detect and correct syntax errors in java source code deepbugs https github com michaelpradel deepbugs framework for learning bug detectors from an existing code corpus deepsim https github com parasol aser deepsim a deep learning based approach to measure code functional similarity rnn autocomplete https github com zerogerc rnn autocomplete neural code autocompletion with rnn bachelor s thesis mindsdb https github com mindsdb mindsdb mindsdb is an explainable automl framework for developers with mindsdb you can build train and use state of the art ml models in as simple as one line of code utilities go git https github com src d go git highly extensible git implementation in pure go which is friendly to data mining bblfsh https github com bblfsh self hosted server for source code parsing engine https github com src d engine scalable and distributed data retrieval pipeline for source code minhashcuda https github com src d minhashcuda weighted minhash implementation on cuda to efficiently find duplicates kmcuda https github com src d kmcuda k means on cuda to cluster and to search for nearest neighbors in dense space wmd relax https github com src d wmd relax python package which finds nearest neighbors at word mover s distance tregex tsurgeon and semgrex https nlp stanford edu software tregex shtml tregex is a utility for matching patterns in trees based on tree relationships and regular expression matches on nodes the name is short for tree regular expressions source d models https github com src d models machine learning models for mloncode trained using the source d stack datasets neural code search evaluation dataset https github com facebookresearch neural code search evaluation dataset dataset contains links to 4 7m methods from 24k repositories with 287 stackoverflow questions and code snippet answers codesearchnet https github com github codesearchnet collection of datasets and benchmarks for code retrieval using natural language contains 2m pairs of comment code public git archive https github com src d datasets tree master publicgitarchive 6 tb of git repositories from github stackoverflow question code dataset https github com littleyuyu stackoverflow question code dataset 148k python and 120k sql question code pairs mined from stackoverflow github issue titles and descriptions for nlp analysis https www kaggle com davidshinn github issues 8 million github issue titles and descriptions from 2017 github repositories languages distribution https data world source d github repositories languages distribution programming languages distribution in 14 000 000 repositories on github october 2016 452m commits on github https data world vmarkovtsev 452 m commits on github 452m commits metadata from 16m repositories on github october 2016 github readme files https data world vmarkovtsev github readme files readme files of all github repositories 16m october 2016 from language x to y https data world vmarkovtsev from language x to y cache file erik bernhardsson collected for his awesome blog post github word2vec 120k https data world vmarkovtsev github word 2 vec 120 k sequences of identifiers extracted from top starred 120 000 github repositories github source code names https data world vmarkovtsev github source code names names in source code extracted from 13m github repositories not people github duplicate repositories https data world vmarkovtsev github duplicate repositories github repositories not marked as forks but very similar to each other github lng keyword frequencies https data world vmarkovtsev github lng keyword frequencies programming language keyword frequency extracted from 16m github repositories github java corpus http groups inf ed ac uk cup javagithub github java corpus is a set of java projects collected from github that we have used in a number of our publications the corpus consists of 14 785 projects and 352 312 696 loc 150k python dataset https www sri inf ethz ch py150 dataset consisting of 150 000 python asts 150k javascript dataset https www sri inf ethz ch js150 dataset consisting of 150 000 javascript files and their parsed asts card2code https github com deepmind card2code this dataset contains the language to code datasets described in the paper latent predictor networks for code generation card2code nl2bash https github com tellinatool nl2bash this dataset contains a set of 10 000 bash one liners collected from websites such as stackoverflow and their english descriptions written by bash programmers as described in the paper https arxiv org abs 1802 08979 github javascript dump october 2016 https archive org details javascript sources oct2016 sqlite3 dataset consisting of 494 352 syntactically valid javascript files obtained from the top 10000 starred javascript repositories on github with licenses and parsed asts bigclonebench https jeffsvajlenko weebly com bigcloneeval html clone detection benchmark of 8 million function clone pairs in the ijadataset credits a lot of references and articles were taken from mast group https mast group github io inspired by awesome machine learning https github com josephmisiti awesome machine learning contributions see contributing md contributing md tl dr create a pull request https github com src d awesome machine learning on source code pulls which is signed off https github com src d awesome machine learning on source code blob master contributing md certificate of origin license license cc by sa 4 0 badges license cc by sa 4 0 lightgrey svg https creativecommons org licenses by sa 4 0 | awesome-list awesome machine-learning machine-learning-on-source-code | ai |
MLclass | mlclass materials for my machine learning class es br given in english or french to doctoral and or master students since 2012 in constant evolution br description this course offers a discovery of the landscape of machine learning through some key algorithms although the first session tries to cover the full span of machine learning techniques the subsequent sessions will focus on the supervized learning problem and will categorize the algorithms from four distinct points of view the bayesian perspective linear separation neural networks and ensemble methods the approach taken mixes voluntarily hands on practice in python with theoretical and mathematical understanding of the methods at the end of the course you will be able to make an informed choice between the main families of ml algorithms depending on the problem at hand you will have an understanding of the algorithmic and mathematical properties of each family of methods and you will have a basic practical knowledge of the scikit learn and keras python libraries course goals by the end of the class you should be able to implement a generic workflow of data analysis for your application field know the main bottlenecks and challenges of data driven approaches link some field problems to their formal machine learning counterparts know the main categories of machine learning algorithms and which formal problem they solve know the name and principles of some key methods in machine learning svm and kernel methods naive bayes classification gaussian processes artificial neural networks and deep learning decision trees ensemble methods boosting bagging random forests know the basics of scikit learn and keras pre requisites basic level in python language fundamentals numpy basic probability and optimization theory you must download and install an anaconda distribution for the latest version of python before the course https anaconda org https anaconda org alternatively to downloading anaconda you ll need a working python installation latest version with at least numpy scipy matplotlib and jupyter installed br if you have a compatible os you can install docker https docs docker com get docker and docker compose for ready to use environments additional required python packages nltk keras conda install keras tensorflow conda install tensorflow graphviz conda install graphviz typical class outline session 1 discovering machine learning an introduction to machine learning a few words on the unsupervized learning problem a few words on the reinforcement learning problem discovering scikit learn session 2 the geometric point of view optimal linear separation support vector machines an introduction to kernel theory session 3 the bayesian point of view the bayes optimal classifier naive bayes classifiers gaussian processes sessions 4 and 5 neuro inspired computation neural networks deep learning convolutional neural networks sessions 6 and 7 ensemble and committee based methods decision trees boosting bagging random forests bibliography the elements of statistical learning br t hastie r tibshirani j friedman br springer series in statistics br https web stanford edu hastie elemstatlearn https web stanford edu hastie elemstatlearn br deep learning br ian goodfellow and yoshua bengio and aaron courville br mit press br https www deeplearningbook org https www deeplearningbook org br more references will be provided during the first session and during classes | machine-learning machine-learning-algorithms supervised-learning course-materials jupyter-notebook | ai |
Generative-AI-with-LLMs | generative ai with llms https www deeplearning ai courses generative ai with llms in generative ai with large language models llms you ll learn the fundamentals of how generative ai works and how to deploy it in real world applications by taking this course you ll learn to deeply understand generative ai describing the key steps in a typical llm based generative ai lifecycle from data gathering and model selection to performance evaluation and deployment describe in detail the transformer architecture that powers llms how they re trained and how fine tuning enables llms to be adapted to a variety of specific use cases use empirical scaling laws to optimize the model s objective function across dataset size compute budget and inference requirements apply state of the art training tuning inference tools and deployment methods to maximize the performance of models within the specific constraints of your project discuss the challenges and opportunities that generative ai creates for businesses after hearing stories from industry researchers and practitioners developers who have a good foundational understanding of how llms work as well the best practices behind training and deploying them will be able to make good decisions for their companies and more quickly build working prototypes this course will support learners in building practical intuition about how to best utilize this exciting new technology week 1 generative ai use cases project lifecycle and model pre training learning objectives discuss model pre training and the value of continued pre training vs fine tuning define the terms generative ai large language models prompt and describe the transformer architecture that powers llms describe the steps in a typical llm based generative ai model lifecycle and discuss the constraining factors that drive decisions at each step of model lifecycle discuss computational challenges during model pre training and determine how to efficiently reduce memory footprint define the term scaling law and describe the laws that have been discovered for llms related to training dataset size compute budget inference requirements and other factors lab 1 generative ai use case summarize dialogue https github com ryota kawamura generative ai with llms blob main week 1 lab 1 summarize dialogue ipynb week 1 quiz https github com ryota kawamura generative ai with llms blob main week 1 week 1 quiz md week 2 fine tuning and evaluating large language models learning objectives describe how fine tuning with instructions using prompt datasets can improve performance on one or more tasks define catastrophic forgetting and explain techniques that can be used to overcome it define the term parameter efficient fine tuning peft explain how peft decreases computational cost and overcomes catastrophic forgetting explain how fine tuning with instructions using prompt datasets can increase llm performance on one or more lab 2 fine tune a generative ai model for dialogue summarization https github com ryota kawamura generative ai with llms blob main week 2 lab 2 fine tune generative ai model ipynb week 2 quiz https github com ryota kawamura generative ai with llms blob main week 2 week 2 quiz md week 3 reinforcement learning and llm powered applications learning objectives describe how rlhf uses human feedback to improve the performance and alignment of large language models explain how data gathered from human labelers is used to train a reward model for rlhf define chain of thought prompting and describe how it can be used to improve llms reasoning and planning abilities discuss the challenges that llms face with knowledge cut offs and explain how information retrieval and augmentation techniques can overcome these challenges lab 3 fine tune flan t5 with reinforcement learning to generate more positive summaries https github com ryota kawamura generative ai with llms blob main week 3 lab 3 fine tune model to detoxify summaries ipynb week 3 quiz https github com ryota kawamura generative ai with llms blob main week 3 week 3 quiz md | generative-ai large-language-models llms machine-learning python-programming | ai |
ethereum_book | https github com ethereumbook ethereumbook mastering ethereum images cover png asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc asciidoc tokens asciidoc dapps asciidoc oracles asciidoc gas asciidoc asciidoc asciidoc vyper asciidoc devp2p asciidoc asciidoc asciidoc images thanks jpeg | ethereum smart-contracts blockchain dapps | blockchain |
Awesome-LLM | awesome llm awesome https awesome re badge svg https awesome re resources image8 gif large language models llm have taken the nlp community ai community the whole world by storm here is a curated list of papers about large language models especially relating to chatgpt it also contains frameworks for llm training tools to deploy llm courses and tutorials about llm and all publicly available llm checkpoints and apis updates 2023 07 01 add some open source models aquila chatglm2 ultra lm 2023 07 01 add some deploying tools vllm text generation inference 2023 07 01 add some great post about llm from yao fu lilian and andrej todos add llm data pretraining data instruction tuning data chat data rlhf data sparkles contributions wanted also check out the project that i am currently working on nanorwkv https github com hannibal046 nanorwkv the nanogpt style implementation of rwkv language model an rnn with gpt level llm performance table of content awesome llm awesome llm updates updates table of content table of content milestone papers milestone papers other papers other papers llm leaderboard llm leaderboard open llm open llm llm training frameworks llm training frameworks tools for deploying llm tools for deploying llm tutorials about llm tutorials about llm courses about llm courses about llm opinions about llm opinions about llm other awesome lists other awesome lists other useful resources other useful resources contributing contributing milestone papers date keywords institute paper publication 2017 06 transformers google attention is all you need https arxiv org pdf 1706 03762 pdf neurips br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f204e3073870fae3d05bcbc2f6a8e263d9b72e776 3ffields 3dcitationcount query 24 citationcount label citation 2018 06 gpt 1 0 openai improving language understanding by generative pre training https www cs ubc ca amuham01 ling530 papers radford2018improving pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fcd18800a0fe0b668a1cc19f2ec95b5003d0a5035 3ffields 3dcitationcount query 24 citationcount label citation 2018 10 bert google bert pre training of deep bidirectional transformers for language understanding https aclanthology org n19 1423 pdf naacl br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fdf2b0e26d0599ce3e70df8a9da02e51594e0e992 3ffields 3dcitationcount query 24 citationcount label citation 2019 02 gpt 2 0 openai language models are unsupervised multitask learners https d4mucfpksywv cloudfront net better language models language models are unsupervised multitask learners pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f9405cc0d6169988371b2755e573cc28650d14dfe 3ffields 3dcitationcount query 24 citationcount label citation 2019 09 megatron lm nvidia megatron lm training multi billion parameter language models using model parallelism https arxiv org pdf 1909 08053 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f8323c591e119eb09b28b29fd6c7bc76bd889df7a 3ffields 3dcitationcount query 24 citationcount label citation 2019 10 t5 google exploring the limits of transfer learning with a unified text to text transformer https jmlr org papers v21 20 074 html jmlr br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f3cfb319689f06bf04c2e28399361f414ca32c4b3 3ffields 3dcitationcount query 24 citationcount label citation 2019 10 zero microsoft zero memory optimizations toward training trillion parameter models https arxiv org pdf 1910 02054 pdf sc br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f00c957711b12468cb38424caccdf5291bb354033 3ffields 3dcitationcount query 24 citationcount label citation 2020 01 scaling law openai scaling laws for neural language models https arxiv org pdf 2001 08361 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fe6c561d02500b2596a230b341a8eb8b921ca5bf2 3ffields 3dcitationcount query 24 citationcount label citation 2020 05 gpt 3 0 openai language models are few shot learners https papers nips cc paper 2020 file 1457c0d6bfcb4967418bfb8ac142f64a paper pdf neurips br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f6b85b63579a916f705a8e10a49bd8d849d91b1fc 3ffields 3dcitationcount query 24 citationcount label citation 2021 01 switch transformers google switch transformers scaling to trillion parameter models with simple and efficient sparsity https arxiv org pdf 2101 03961 pdf jmlr br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2ffdacf2a732f55befdc410ea927091cad3b791f13 3ffields 3dcitationcount query 24 citationcount label citation 2021 08 codex openai evaluating large language models trained on code https arxiv org pdf 2107 03374 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2facbdbf49f9bc3f151b93d9ca9a06009f4f6eb269 3ffields 3dcitationcount query 24 citationcount label citation 2021 08 foundation models stanford on the opportunities and risks of foundation models https arxiv org pdf 2108 07258 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f4f68e07c6c3173480053fd52391851d6f80d651b 3ffields 3dcitationcount query 24 citationcount label citation 2021 09 flan google finetuned language models are zero shot learners https openreview net forum id gezrgcozdqr iclr br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fff0b2681d7b05e16c46dfb71d980cc2f605907cd 3ffields 3dcitationcount query 24 citationcount label citation 2021 10 t0 huggingface et al multitask prompted training enables zero shot task generalization https arxiv org abs 2110 08207 iclr br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f17dd3555fd1ccf1141cf984347fa1b3fd6b009ca 3ffields 3dcitationcount query 24 citationcount label citation 2021 12 glam google glam efficient scaling of language models with mixture of experts https arxiv org pdf 2112 06905 pdf icml br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f80d0116d77beeded0c23cf48946d9d10d4faee14 3ffields 3dcitationcount query 24 citationcount label citation 2021 12 webgpt openai webgpt browser assisted question answering with human feedback https www semanticscholar org paper webgpt 3a browser assisted question answering with nakano hilton 2f3efe44083af91cef562c1a3451eee2f8601d22 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f2f3efe44083af91cef562c1a3451eee2f8601d22 3ffields 3dcitationcount query 24 citationcount label citation 2021 12 retro deepmind improving language models by retrieving from trillions of tokens https www deepmind com publications improving language models by retrieving from trillions of tokens icml br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f002c256d30d6be4b23d365a8de8ae0e67e4c9641 3ffields 3dcitationcount query 24 citationcount label citation 2021 12 gopher deepmind scaling language models methods analysis amp insights from training gopher https arxiv org pdf 2112 11446 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f68f141724814839d556a989646194be88641b143 3ffields 3dcitationcount query 24 citationcount label citation 2022 01 cot google chain of thought prompting elicits reasoning in large language models https arxiv org pdf 2201 11903 pdf neurips br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f1b6e810ce0afd0dd093f789d2b2742d047e316d5 3ffields 3dcitationcount query 24 citationcount label citation 2022 01 lamda google lamda language models for dialog applications https arxiv org pdf 2201 08239 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fb3848d32f7294ec708627897833c4097eb4d8778 3ffields 3dcitationcount query 24 citationcount label citation 2022 01 minerva google solving quantitative reasoning problems with language models https arxiv org abs 2206 14858 neurips br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fab0e3d3e4d42369de5933a3b4c237780b41c0d77 3ffields 3dcitationcount query 24 citationcount label citation 2022 01 megatron turing nlg microsoft nvidia using deepspeed and megatron to train megatron turing nlg 530b a large scale generative language model https arxiv org pdf 2201 11990 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f7cbc2a7843411a1768ab762930707af0a3c33a19 3ffields 3dcitationcount query 24 citationcount label citation 2022 03 instructgpt openai training language models to follow instructions with human feedback https arxiv org pdf 2203 02155 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fd766bffc357127e0dc86dd69561d5aeb520d6f4c 3ffields 3dcitationcount query 24 citationcount label citation 2022 04 palm google palm scaling language modeling with pathways https arxiv org pdf 2204 02311 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f094ff971d6a8b8ff870946c9b3ce5aa173617bfb 3ffields 3dcitationcount query 24 citationcount label citation 2022 04 chinchilla deepmind an empirical analysis of compute optimal large language model training https www deepmind com publications an empirical analysis of compute optimal large language model training neurips br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fbb0656031cb17adf6bac5fd0fe8d53dd9c291508 3ffields 3dcitationcount query 24 citationcount label citation 2022 05 opt meta opt open pre trained transformer language models https arxiv org pdf 2205 01068 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f13a0d8bb38f739990c8cd65a44061c6534f17221 3ffields 3dcitationcount query 24 citationcount label citation 2022 05 ul2 google unifying language learning paradigms https arxiv org abs 2205 05131v1 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2ff40aeae3e522ada1f6a9f326841b01ef5c8657b6 3ffields 3dcitationcount query 24 citationcount label citation 2022 06 emergent abilities google emergent abilities of large language models https openreview net pdf id yzksu5zdwd tmlr br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fdac3a172b504f4e33c029655e9befb3386e5f63a 3ffields 3dcitationcount query 24 citationcount label citation 2022 06 big bench google beyond the imitation game quantifying and extrapolating the capabilities of language models https github com google big bench dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f34503c0b6a615124eaf82cb0e4a1dab2866e8980 3ffields 3dcitationcount query 24 citationcount label citation 2022 06 metalm microsoft language models are general purpose interfaces https arxiv org pdf 2206 06336 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fa8fd9c1625011741f74401ff9bdc1c584e25c86d 3ffields 3dcitationcount query 24 citationcount label citation 2022 09 sparrow deepmind improving alignment of dialogue agents via targeted human judgements https arxiv org pdf 2209 14375 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f74eae12620bd1c1393e268bddcb6f129a5025166 3ffields 3dcitationcount query 24 citationcount label citation 2022 10 flan t5 palm google scaling instruction finetuned language models https arxiv org pdf 2210 11416 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f5484d228bfc50efbac6e86677bc2ec2ee4ede1a6 3ffields 3dcitationcount query 24 citationcount label citation 2022 10 glm 130b tsinghua glm 130b an open bilingual pre trained model https arxiv org pdf 2210 02414 pdf iclr br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f1d26c947406173145a4665dd7ab255e03494ea28 3ffields 3dcitationcount query 24 citationcount label citation 2022 11 helm stanford holistic evaluation of language models https arxiv org pdf 2211 09110 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f5032c0946ee96ff11a292762f23e6377a6cf2731 3ffields 3dcitationcount query 24 citationcount label citation 2022 11 bloom bigscience bloom a 176b parameter open access multilingual language model https arxiv org pdf 2211 05100 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f964bd39b546f0f6625ff3b9ef1083f797807ef2e 3ffields 3dcitationcount query 24 citationcount label citation 2022 11 galactica meta galactica a large language model for science https arxiv org pdf 2211 09085 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f7d645a3fd276918374fd9483fd675c28e46506d1 3ffields 3dcitationcount query 24 citationcount label citation 2022 12 opt iml meta opt iml scaling language model instruction meta learning through the lens of generalization https arxiv org pdf 2212 12017 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fe965e93e76a9e6c4e4863d145b5c007b540d575d 3ffields 3dcitationcount query 24 citationcount label citation 2023 01 flan 2022 collection google the flan collection designing data and methods for effective instruction tuning https arxiv org pdf 2301 13688 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2ff2b0017ddd77fa38760a18145e63553105a1a236 3ffields 3dcitationcount query 24 citationcount label citation 2023 02 llama meta llama open and efficient foundation language models https research facebook com publications llama open and efficient foundation language models dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f57e849d0de13ed5f91d086936296721d4ff75a75 3ffields 3dcitationcount query 24 citationcount label citation 2023 02 kosmos 1 microsoft language is not all you need aligning perception with language models https arxiv org abs 2302 14045 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2ffbfef4723d8c8467d7bd523e1d0b703cce0e0f9c 3ffields 3dcitationcount query 24 citationcount label citation 2023 03 palm e google palm e an embodied multimodal language model https palm e github io dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f38fe8f324d2162e63a967a9ac6648974fc4c66f3 3ffields 3dcitationcount query 24 citationcount label citation 2023 03 gpt 4 openai gpt 4 technical report https openai com research gpt 4 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f8ca62fdf4c276ea3052dc96dcfd8ee96ca425a48 3ffields 3dcitationcount query 24 citationcount label citation 2023 04 pythia eleutherai et al pythia a suite for analyzing large language models across training and scaling https arxiv org abs 2304 01373 icml br dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fbe55e8ec4213868db08f2c3168ae666001bea4b8 3ffields 3dcitationcount query 24 citationcount label citation 2023 05 dromedary cmu et al principle driven self alignment of language models from scratch with minimal human supervision https arxiv org abs 2305 03047 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2fe01515c6138bc525f7aec30fc85f2adf028d4156 3ffields 3dcitationcount query 24 citationcount label citation 2023 05 palm 2 google palm 2 technical report https ai google static documents palm2techreport pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2feccee350691708972370b7a12c2a78ad3bddd159 3ffields 3dcitationcount query 24 citationcount label citation 2023 05 rwkv bo peng rwkv reinventing rnns for the transformer era https arxiv org abs 2305 13048 dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f026b3396a63ed5772329708b7580d633bb86bec9 3ffields 3dcitationcount query 24 citationcount label citation 2023 05 dpo stanford direct preference optimization your language model is secretly a reward model https arxiv org pdf 2305 18290 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f0d1c76d45afa012ded7ab741194baf142117c495 3ffields 3dcitationcount query 24 citationcount label citation 2023 07 llama 2 meta llama 2 open foundation and fine tuned chat models https arxiv org pdf 2307 09288 pdf dynamic json badge https img shields io badge dynamic json url https 3a 2f 2fapi semanticscholar org 2fgraph 2fv1 2fpaper 2f104b0bb1da562d53cbda87aec79ef6a2827d191a 3ffields 3dcitationcount query 24 citationcount label citation other papers if you re interested in the field of llm you may find the above list of milestone papers helpful to explore its history and state of the art however each direction of llm offers a unique set of insights and contributions which are essential to understanding the field as a whole for a detailed list of papers in various subfields please refer to the following link it is possible that there are overlaps between different subfields exclamation we would greatly appreciate and welcome your contribution to the following list exclamation llm analysis paper list evaluation md analyse different llms in different fields with respect to different abilities llm acceleration paper list acceleration md hardware and software acceleration for llm training and inference llm application paper list application md use llm to do some really cool stuff llm augmentation paper list augmentation md augment llm in different aspects including faithfulness expressiveness domain specific knowledge etc llm detection paper list detection md detect llm generated text from texts written by humans llm alignment paper list alignment md align llm with human preference chain of thought paper list chain of thougt md chain of thought a series of intermediate reasoning steps significantly improves the ability of large language models to perform complex reasoning in context learning paper list in context learning md large language models llms demonstrate an in context learning icl ability that is learning from a few examples in the context prompt learning paper list prompt learning md a good prompt is worth 1 000 words instruction tuning paper list instruction tuning md finetune a language model on a collection of tasks described via instructions retrieval augmented generation paper list retrieval augmented generation md retrieval augmented generation rag combines retrieval from a corpus with generative text models to enhance response accuracy using external knowledge llm leaderboard div align center img src resources creepy llm jpeg width 500 div there are three important steps for a chatgpt like llm 1 pre training 2 instruction tuning 3 alignment the following list makes sure that all llms are compared apples to apples you may also find these leaderboards helpful open llm leaderboard https huggingface co spaces huggingfaceh4 open llm leaderboard aims to track rank and evaluate llms and chatbots as they are released chatbot arena leaderboard https huggingface co spaces lmsys chatbot arena leaderboard a benchmark platform for large language models llms that features anonymous randomized battles in a crowdsourced manner alpacaeval leaderboard https tatsu lab github io alpaca eval an automatic evaluator for instruction following language models open ko llm leaderboard https huggingface co spaces upstage open ko llm leaderboard the open ko llm leaderboard objectively evaluates the performance of korean large language model llm base llm model size architecture access date origin model license 1 switch transformer 1 6t decoder moe 2021 01 paper https arxiv org pdf 2101 03961 pdf glam 1 2t decoder moe 2021 12 paper https arxiv org pdf 2112 06905 pdf palm 540b decoder 2022 04 paper https arxiv org pdf 2204 02311 pdf mt nlg 530b decoder 2022 01 paper https arxiv org pdf 2201 11990 pdf j1 jumbo 178b decoder api https docs ai21 com docs complete api 2021 08 paper https uploads ssl webflow com 60fd4503684b466578c0d307 61138924626a6981ee09caf6 jurassic tech paper pdf opt 175b decoder api https opt alpa ai ckpt https github com facebookresearch metaseq tree main projects opt 2022 05 paper https arxiv org pdf 2205 01068 pdf opt 175b license agreement https github com facebookresearch metaseq blob edefd4a00c24197486a3989abe28ca4eb3881e59 projects opt model license md bloom 176b decoder api https huggingface co bigscience bloom ckpt https huggingface co bigscience bloom 2022 11 paper https arxiv org pdf 2211 05100 pdf bigscience rail license v1 0 https huggingface co spaces bigscience license gpt 3 0 175b decoder api https openai com api 2020 05 paper https arxiv org pdf 2005 14165 pdf lamda 137b decoder 2022 01 paper https arxiv org pdf 2201 08239 pdf glm 130b decoder ckpt https github com thudm glm 130b 2022 10 paper https arxiv org pdf 2210 02414 pdf the glm 130b license https github com thudm glm 130b blob 799837802264eb9577eb9ae12cd4bad0f355d7d6 model license yalm 100b decoder ckpt https github com yandex yalm 100b 2022 06 blog https medium com yandex yandex publishes yalm 100b its the largest gpt like neural network in open source d1df53d0e9a6 apache 2 0 https github com yandex yalm 100b blob 14fa94df2ebbbd1864b81f13978f2bf4af270fcb license llama 65b decoder ckpt https github com facebookresearch llama 2022 09 paper https research facebook com publications llama open and efficient foundation language models non commercial bespoke license https github com facebookresearch llama blob main model card md gpt neox 20b decoder ckpt https github com eleutherai gpt neox 2022 04 paper https arxiv org pdf 2204 06745 pdf apache 2 0 https github com eleutherai gpt neox blob main license falcon 40b decoder ckpt https huggingface co tiiuae falcon 40b 2023 05 homepage https falconllm tii ae apache 2 0 https huggingface co tiiuae ul2 20b agnostic ckpt https huggingface co google ul2 text ul2 20is 20a 20unified 20framework 20for 20pretraining 20models downstream 20fine tuning 20is 20associated 20with 20specific 20pre training 20schemes 2022 05 paper https arxiv org pdf 2205 05131v1 pdf apache 2 0 https huggingface co google ul2 13b decoder ckpt https github com huawei noah pretrained language model tree master pangu 2021 04 paper https arxiv org pdf 2104 12369 pdf apache 2 0 https github com huawei noah pretrained language model blob 4624dbadfe00e871789b509fe10232c77086d1de pangu ce b1 license t5 11b encoder decoder ckpt https huggingface co t5 11b 2019 10 paper https jmlr org papers v21 20 074 html apache 2 0 https huggingface co t5 11b cpm bee 10b decoder api https live openbmb org models bee 2022 10 paper https arxiv org pdf 2012 00413 pdf rwkv 4 7b rwkv ckpt https huggingface co blinkdl rwkv 4 pile 7b 2022 09 github https github com blinkdl rwkv lm apache 2 0 https huggingface co blinkdl rwkv 4 pile 7b gpt j 6b decoder ckpt https huggingface co eleutherai gpt j 6b 2022 09 github https github com kingoflolz mesh transformer jax apache 2 0 https huggingface co eleutherai gpt j 6b gpt neo 2 7b decoder ckpt https github com eleutherai gpt neo 2021 03 github https github com eleutherai gpt neo mit https github com eleutherai gpt neo blob 23485e3c7940560b3b4cb12e0016012f14d03fc7 license gpt neo 1 3b decoder ckpt https github com eleutherai gpt neo 2021 03 github https github com eleutherai gpt neo mit https github com eleutherai gpt neo blob 23485e3c7940560b3b4cb12e0016012f14d03fc7 license instruction finetuned llm model size architecture access date origin model license 1 flan palm 540b decoder 2022 10 paper https arxiv org pdf 2210 11416 pdf bloomz 176b decoder ckpt https huggingface co bigscience bloomz 2022 11 paper https arxiv org pdf 2211 01786 pdf bigscience rail license v1 0 https huggingface co spaces bigscience license instructgpt 175b decoder api https platform openai com overview 2022 03 paper https arxiv org pdf 2203 02155 pdf galactica 120b decoder ckpt https huggingface co facebook galactica 120b 2022 11 paper https arxiv org pdf 2211 09085 pdf cc by nc 4 0 https github com paperswithcode galai blob 3a724f562af1a0c8ff97a096c5fbebe579e2160f license model md openchatkit 20b ckpt https github com togethercomputer openchatkit 2023 3 apache 2 0 https github com togethercomputer openchatkit blob e64116eb569fcb4e0b993c5fab5716abcb08c7e5 license flan ul2 20b decoder ckpt https github com google research google research tree master ul2 2023 03 blog https www yitay net blog flan ul2 20b apache 2 0 https huggingface co google flan ul2 gopher chinchilla flan t5 11b encoder decoder ckpt https github com google research t5x blob main docs models md flan t5 checkpoints 2022 10 paper https arxiv org pdf 2210 11416 pdf apache 2 0 https github com google research t5x blob 776279bdacd8c5a2d3e8ce0f2e7064bd98e98b47 license t0 11b encoder decoder ckpt https huggingface co bigscience t0 2021 10 paper https arxiv org pdf 2110 08207 pdf apache 2 0 https huggingface co bigscience t0 alpaca 7b decoder demo https crfm stanford edu alpaca 2023 03 github https github com tatsu lab stanford alpaca cc by nc 4 0 https github com tatsu lab stanford alpaca blob main weight diff license orca 13b decoder ckpt https aka ms orca 1m 2023 06 paper https arxiv org pdf 2306 02707 non commercial bespoke license https github com facebookresearch llama blob main model card md rlhf llm model size architecture access date origin gpt 4 2023 03 blog https openai com research gpt 4 chatgpt decoder demo https openai com blog chatgpt api https share hsforms com 1u4goaxwdrkc9 x9ivkno0a4sk30 2022 11 blog https openai com blog chatgpt sparrow 70b 2022 09 paper https arxiv org pdf 2209 14375 pdf claude demo https poe com claude api https www anthropic com earlyaccess 2023 03 blog https www anthropic com index introducing claude the above tables coule be better summarized by this wonderful visualization from this survey paper https arxiv org abs 2304 13712 p align center img width 600 src https github com mooler0410 llmspracticalguide blob main imgs survey gif test gif p open llm llama https ai facebook com blog large language model llama meta ai a foundational 65 billion parameter large language model llama cpp https github com ggerganov llama cpp lit llama https github com lightning ai lit llama alpaca https crfm stanford edu 2023 03 13 alpaca html a model fine tuned from the llama 7b model on 52k instruction following demonstrations alpaca cpp https github com antimatter15 alpaca cpp alpaca lora https github com tloen alpaca lora flan alpaca https github com declare lab flan alpaca instruction tuning from humans and machines baize https github com project baize baize chatbot baize is an open source chat model trained with lora https github com microsoft lora it uses 100k dialogs generated by letting chatgpt chat with itself cabrita https github com 22 hours cabrita a portuguese finetuned instruction llama vicuna https lmsys org blog 2023 03 30 vicuna an open source chatbot impressing gpt 4 with 90 chatgpt quality llama x https github com aethercortex llama x open academic research on improving llama to sota llm chinese vicuna https github com facico chinese vicuna a chinese instruction following llama based model gptq for llama https github com qwopqwop200 gptq for llama 4 bits quantization of llama https arxiv org abs 2302 13971 using gptq https arxiv org abs 2210 17323 gpt4all https github com nomic ai gpt4all demo data and code to train open source assistant style large language model based on gpt j and llama koala https bair berkeley edu blog 2023 04 03 koala a dialogue model for academic research belle https github com lianjiatech belle be everyone s large language model engine stackllama https huggingface co blog stackllama a hands on guide to train llama with rlhf redpajama https github com togethercomputer redpajama data an open source recipe to reproduce llama training dataset chimera https github com freedomintelligence llmzoo latin phoenix wizardlm wizardcoder https github com nlpxucan wizardlm family of instruction following llms powered by evol instruct wizardlm wizardcoder cama https github com zjunlp cama a chinese english bilingual llama model orca https aka ms orca lm microsoft s finetuned llama model that reportedly matches gpt3 5 finetuned against 5m of data chatgpt and gpt4 bayling https github com ictnlp bayling an english chinese llm equipped with advanced language alignment showing superior capability in english chinese generation instruction following and multi turn interaction ultralm https github com thunlp ultrachat large scale informative and diverse multi round chat models guanaco https github com artidoro qlora qlora tuned llama bloom https huggingface co bigscience bloom bigscience large open science open access multilingual language model bloom lora https github com linhduongtuan bloom lora bloomz mt0 https huggingface co bigscience bloomz a family of models capable of following human instructions in dozens of languages zero shot phoenix https github com freedomintelligence llmzoo t5 https arxiv org abs 1910 10683 text to text transfer transformer t0 https arxiv org abs 2110 08207 multitask prompted training enables zero shot task generalization opt https arxiv org abs 2205 01068 open pre trained transformer language models ul2 https arxiv org abs 2205 05131v1 a unified framework for pretraining models that are universally effective across datasets and setups glm https github com thudm glm glm is a general language model pretrained with an autoregressive blank filling objective and can be finetuned on various natural language understanding and generation tasks chatglm 6b https github com thudm chatglm 6b chatglm 6b general language model glm https github com thudm glm 62 chatglm2 6b https github com thudm chatglm2 6b an open bilingual chat llm rwkv https github com blinkdl rwkv lm parallelizable rnn with transformer level llm performance chatrwkv https github com blinkdl chatrwkv chatrwkv is like chatgpt but powered by my rwkv 100 rnn language model stablelm https stability ai blog stability ai launches the first of its stablelm suite of language models stability ai language models yalm https medium com yandex yandex publishes yalm 100b its the largest gpt like neural network in open source d1df53d0e9a6 a gpt like neural network for generating and processing text it can be used freely by developers and researchers from all over the world gpt neo https github com eleutherai gpt neo an implementation of model data parallel gpt3 https arxiv org abs 2005 14165 like models using the mesh tensorflow https github com tensorflow mesh library gpt j https github com kingoflolz mesh transformer jax gpt j 6b a 6 billion parameter autoregressive text generation model trained on the pile https pile eleuther ai dolly https www databricks com blog 2023 03 24 hello dolly democratizing magic chatgpt open models html a cheap to build llm that exhibits a surprising degree of the instruction following capabilities exhibited by chatgpt pythia https github com eleutherai pythia interpreting autoregressive transformers across time and scale dolly 2 0 https www databricks com blog 2023 04 12 dolly first open commercially viable instruction tuned llm the first open source instruction following llm fine tuned on a human generated instruction dataset licensed for research and commercial use openflamingo https github com mlfoundations open flamingo an open source reproduction of deepmind s flamingo model cerebras gpt https www cerebras net blog cerebras gpt a family of open compute efficient large language models a family of open compute efficient large language models galactica https github com paperswithcode galai blob main docs model card md the galactica models are trained on a large scale scientific corpus galpaca https huggingface co georgiatechresearchinstitute galpaca 30b galactica 30b fine tuned on the alpaca dataset palmyra https huggingface co writer palmyra base palmyra base was primarily pre trained with english text camel https huggingface co writer camel 5b hf a state of the art instruction following large language model designed to deliver exceptional performance and versatility h2ogpt https github com h2oai h2ogpt pangu https openi org cn pangu pangu is a 200b parameter autoregressive pretrained chinese language model develped by huawei noah s ark lab mindspore team and peng cheng laboratory moss https github com openlmlab moss moss open assistant https github com laion ai open assistant a project meant to give everyone access to a great chat based large language model huggingchat https huggingface co chat powered by open assistant s latest model the best open source chat model right now and huggingface inference api starcoder https huggingface co blog starcoder hugging face llm for code mpt 7b https www mosaicml com blog mpt 7b open llm for commercial use by mosaicml falcon https falconllm tii ae falcon llm is a foundational large language model llm with 40 billion parameters trained on one trillion tokens tii has now released falcon llm a 40b model xgen https github com salesforce xgen salesforce open source llms with 8k sequence length baichuan 7b https github com baichuan inc baichuan 7b baichuan 7b aquila https github com flagai open flagai tree master examples aquila llm training frameworks deepspeed https github com microsoft deepspeed deepspeed is a deep learning optimization library that makes distributed training and inference easy efficient and effective megatron deepspeed https github com microsoft megatron deepspeed deepspeed version of nvidia s megatron lm that adds additional support for several features such as moe model training curriculum learning 3d parallelism and others fairscale https fairscale readthedocs io en latest what is fairscale html fairscale is a pytorch extension library for high performance and large scale training megatron lm https github com nvidia megatron lm ongoing research training transformer models at scale colossal ai https github com hpcaitech colossalai making large ai models cheaper faster and more accessible bmtrain https github com openbmb bmtrain efficient training for big models mesh tensorflow https github com tensorflow mesh mesh tensorflow model parallelism made easier maxtext https github com google maxtext a simple performant and scalable jax llm alpa https alpa ai index html alpa is a system for training and serving large scale neural networks gpt neox https github com eleutherai gpt neox an implementation of model parallel autoregressive transformers on gpus based on the deepspeed library tools for deploying llm fastchat https github com lm sys fastchat a distributed multi model llm serving system with web ui and openai compatible restful apis skypilot https github com skypilot org skypilot run llms and batch jobs on any cloud get maximum cost savings highest gpu availability and managed execution all with a simple interface vllm https github com vllm project vllm a high throughput and memory efficient inference and serving engine for llms text generation inference https github com huggingface text generation inference a rust python and grpc server for text generation inference used in production at huggingface https huggingface co to power llms api inference widgets haystack https haystack deepset ai an open source nlp framework that allows you to use llms and transformer based models from hugging face openai and cohere to interact with your own data sidekick https github com ai sidekick sidekick data integration platform for llms langchain https github com hwchase17 langchain building applications with llms through composability litechain https github com rogeriochaves litechain lightweight alternative to langchain for composing llms magentic https github com jackmpcollins magentic seamlessly integrate llms as python functions wechat chatgpt https github com fuergaosi233 wechat chatgpt use chatgpt on wechat via wechaty promptfoo https github com typpo promptfoo test your prompts evaluate and compare llm outputs catch regressions and improve prompt quality agenta https github com agenta ai agenta easily build version evaluate and deploy your llm powered apps serge https github com serge chat serge a chat interface crafted with llama cpp for running alpaca models no api keys entirely self hosted langroid https github com langroid langroid harness llms with multi agent programming embedchain https github com embedchain embedchain framework to create chatgpt like bots over your dataset cometllm https github com comet ml comet llm a 100 opensource llmops platform to log manage and visualize your llm prompts and chains track prompt templates prompt variables prompt duration token usage and other metadata score prompt outputs and visualize chat history all within a single ui intelliserver https github com intelligentnode intelliserver simplifies the evaluation of llms by providing a unified microservice to access and test multiple ai models openllm https github com bentoml openllm fine tune serve deploy and monitor any open source llms in production used in production at bentoml https bentoml com for llms based applications prompting libraries tools yival https github com yival yival evaluate and evolve yival is an open source genai ops tool for tuning and evaluating prompts configurations and model parameters using customizable datasets evaluation methods and improvement strategies guidance https github com microsoft guidance a handy looking python library from microsoft that uses handlebars templating to interleave generation prompting and logical control langchain https github com hwchase17 langchain a popular python javascript library for chaining sequences of language model prompts flaml a fast library for automated machine learning tuning https microsoft github io flaml docs getting started a python library for automating selection of models hyperparameters and other tunable choices chainlit https docs chainlit io overview a python library for making chatbot interfaces guardrails ai https shreyar github io guardrails a python library for validating outputs and retrying failures still in alpha so expect sharp edges and bugs semantic kernel https github com microsoft semantic kernel a python c java library from microsoft that supports prompt templating function chaining vectorized memory and intelligent planning prompttools https github com hegelai prompttools open source python tools for testing and evaluating models vector dbs and prompts outlines https github com normal computing outlines a python library that provides a domain specific language to simplify prompting and constrain generation promptify https github com promptslab promptify a small python library for using language models to perform nlp tasks scale spellbook https scale com spellbook a paid product for building comparing and shipping language model apps promptperfect https promptperfect jina ai prompts a paid product for testing and improving prompts weights biases https wandb ai site solutions llmops a paid product for tracking model training and prompt engineering experiments openai evals https github com openai evals an open source library for evaluating task performance of language models and prompts llamaindex https github com jerryjliu llama index a python library for augmenting llm apps with data arthur shield https www arthur ai get started a paid product for detecting toxicity hallucination prompt injection etc lmql https lmql ai a programming language for llm interaction with support for typed prompting control flow constraints and tools modelfusion https github com lgrammel modelfusion a typescript library for building apps with llms and other ml models speech to text text to speech image generation flappy https github com pleisto flappy production ready llm agent sdk for every developer tutorials about llm andrej karpathy state of gpt video https build microsoft com en us sessions db3f4859 cd30 4445 a0cd 553c3304f8e2 hyung won chung instruction finetuning and rlhf lecture youtube https www youtube com watch v zjrm mw 0y0 jason wei scaling emergence and reasoning in large language models slides https docs google com presentation d 1euv7w7x w0bdrscdhpg7lmgzjckeapkgcj3bn8dluxc edit pli 1 resourcekey 0 7nz5a7y8jozyvrndtcekja slide id g16197112905 0 0 susan zhang open pretrained transformers youtube https www youtube com watch v p9ixoskvz m t 4s ameet deshpande how does chatgpt work slides https docs google com presentation d 1ttyeprw p xxubi3rbmbi3qqpssti1btaquauvvnc8w edit slide id g206fa25c94c 0 24 yao fu bilibili https www bilibili com video bv1qs4y1h7pn spm id from 333 337 search card all click vd source 1e55c5426b48b37e901ff0f78992e33f hung yi lee chatgpt youtube https www youtube com watch v yiy4npozjeg list rdcmuc2ggjtuuwvxrhhhiadh1dlq index 2 jay mody gpt in 60 lines of numpy link https jaykmody com blog gpt from scratch icml 2022 welcome to the 34 big model 34 era techniques and systems to train and serve bigger models link https icml cc virtual 2022 tutorial 18440 neurips 2022 foundational robustness of foundation models link https nips cc virtual 2022 tutorial 55796 andrej karpathy let s build gpt from scratch in code spelled out video https www youtube com watch v kcc8fmeb1ny code https github com karpathy ng video lecture dair ai prompt engineering guide link https github com dair ai prompt engineering guide slides resources 20 2030min pdf video https www bilibili com video bv1xb411x7c3 buvid xy2da82257cc34decd40b00cae8afb7f3b43c is story h5 false mid dm1ovipeco22etytwkjvvg 3d 3d p 1 plat id 116 share from ugc share medium android share plat android share session id c42b6c60 9d22 4c75 90b8 48828e1168af share source weixin share tag s i timestamp 1676812375 unique k mehb9xg up id 487788801 vd source 1e55c5426b48b37e901ff0f78992e33f philipp schmid fine tune flan t5 xl xxl using deepspeed hugging face transformers link https www philschmid de fine tune flan t5 deepspeed huggingface illustrating reinforcement learning from human feedback rlhf link https huggingface co blog rlhf huggingface what makes a dialog agent useful link https huggingface co blog dialog agents agi llm link https zhuanlan zhihu com p 597586623 chatgpt instructgpt link https zhuanlan zhihu com p 590311003 heptaai chatgpt instructgpt ppo link https zhuanlan zhihu com p 589747432 yao fu how does gpt obtain its ability tracing emergent abilities of language models to their sources link https yaofu notion site how does gpt obtain its ability tracing emergent abilities of language models to their sources b9a57ac0fcf74f30a1ab9e3e36fa1dc1 stephen wolfram what is chatgpt doing and why does it work link https writings stephenwolfram com 2023 02 what is chatgpt doing and why does it work jingfeng yang why did all of the public reproduction of gpt 3 fail link https jingfengyang github io gpt hung yi lee chatgpt gpt video https www youtube com watch v e0aki2ggzng keyvan kambakhsh pure rust implementation of a minimal generative pretrained transformer code https github com keyvank femtogpt llm link https www zhihu com column c 1252604770952642560 courses about llm deeplearning ai chatgpt prompt engineering for developers homepage https www deeplearning ai short courses chatgpt prompt engineering for developers princeton understanding large language models homepage https www cs princeton edu courses archive fall22 cos597g openbmb https www openbmb org community course stanford cs224n lecture 11 prompting instruction finetuning and rlhf slides https web stanford edu class cs224n slides cs224n 2023 lecture11 prompting rlhf pdf stanford cs324 large language models homepage https stanford cs324 github io winter2022 stanford cs25 transformers united v2 homepage https web stanford edu class cs25 stanford webinar gpt 3 beyond video https www youtube com watch v lnhhwrcdgk instructgpt bilibili https www bilibili com video bv1hd4y187cr spm id from 333 337 search card all click vd source 1e55c5426b48b37e901ff0f78992e33f youtube https www youtube com watch v zfigawd1joq openai instructgpt chatgpt youtube https www youtube com watch v orhv8ykav2q helm bilibili https www bilibili com video bv1z24y1b7ux spm id from 333 337 search card all click vd source 1e55c5426b48b37e901ff0f78992e33f gpt gpt 2 gpt 3 bilibili https www bilibili com video bv1af411b7xq spm id from 333 788 vd source 1e55c5426b48b37e901ff0f78992e33f youtube https www youtube com watch v t70bl3w7bxy list plfxj6jwg0qw 7um8iutj3qkqdhbqulp5i index 18 aston zhang chain of thought bilibili https www bilibili com video bv1t8411e7ug spm id from 333 788 vd source 1e55c5426b48b37e901ff0f78992e33f youtube https www youtube com watch v h4j59ig3t5o list plfxj6jwg0qw 7um8iutj3qkqdhbqulp5i index 29 mit introduction to data centric ai homepage https dcai csail mit edu opinions about llm a stage review of instruction tuning https yaofu notion site june 2023 a stage review of instruction tuning f59dbfc36e2d4e12a33443bd6b2012c2 2023 06 29 yao fu llm powered autonomous agents https lilianweng github io posts 2023 06 23 agent 2023 06 23 lilian why you should work on ai agents https www youtube com watch v fqvljtvwgq8 2023 06 22 andrej karpathy google we have no moat and neither does openai https www semianalysis com p google we have no moat and neither 2023 05 05 ai competition statement https petergabriel com news ai competition statement 2023 04 20 petergabriel https mp weixin qq com s zvyxrpgia4l4pqfcqtptq 2023 04 23 prompt engineering https lilianweng github io posts 2023 03 15 prompt engineering 2023 03 15 lilian noam chomsky the false promise of chatgpt https www nytimes com 2023 03 08 opinion noam chomsky chatgpt ai html 2023 03 08 noam chomsky is chatgpt 175 billion parameters technical analysis https orenleung super site is chatgpt 175 billion parameters technical analysis 2023 03 04 owen towards chatgpt and beyond https zhuanlan zhihu com p 607637180 2023 02 20 chatgpt https mp weixin qq com s eymssapfodjc7xwh1jhydq 2023 02 19 rumor chatgpt https zhuanlan zhihu com p 606918875 2023 02 16 chatgpt https zhuanlan zhihu com p 606478660 2023 02 15 chatgpt https zhuanlan zhihu com p 590655677 utm source wechat session utm medium social utm oi 714896487502315520 s r 0 2023 02 15 chatgpt https zhuanlan zhihu com p 605882945 utm medium social utm oi 939485757606461440 utm psn 1609870392121860096 utm source wechat session 2023 02 13 chatgpt https zhuanlan zhihu com p 605331104 2023 02 11 nlp the next generation of large language models https www notion so awesome llm 40c8aa3f2b444ecc82b79ae8bbd2696b 2023 02 07 forbes large language model training in 2023 https research aimultiple com large language model training 2023 02 03 cem dilmegani what are large language models used for https www notion so awesome llm 40c8aa3f2b444ecc82b79ae8bbd2696b 2023 01 26 nvidia large language models a new moore 39 s law https huggingface co blog large language models 2021 10 26 huggingface other awesome lists llmspracticalguide https github com mooler0410 llmspracticalguide a curated still actively updated list of practical guide resources of llms awesome chatgpt prompts https github com f awesome chatgpt prompts a collection of prompt examples to be used with the chatgpt model awesome chatgpt prompts zh https github com plexpt awesome chatgpt prompts zh a chinese collection of prompt examples to be used with the chatgpt model awesome chatgpt https github com humanloop awesome chatgpt curated list of resources for chatgpt and gpt 3 from openai chain of thoughts papers https github com timothyxxx chain of thoughtspapers a trend starts from chain of thought prompting elicits reasoning in large language models instruction tuning papers https github com sinclaircoder instruction tuning papers a trend starts from natrural instruction acl 2022 flan iclr 2022 and t0 iclr 2022 llm reading list https github com crazyofapple reading groups a paper resource list of large language models reasoning using language models https github com atfortes lm reasoning papers collection of papers and resources on reasoning using language models chain of thought hub https github com franxyao chain of thought hub measuring llms reasoning performance awesome gpt https github com formulahendry awesome gpt a curated list of awesome projects and resources related to gpt chatgpt openai llm and more awesome gpt 3 https github com elyase awesome gpt3 a collection of demos and articles about the openai gpt 3 api https openai com blog openai api awesome llm human preference datasets https github com polisai awesome llm human preference datasets a collection of human preference datasets for llm instruction tuning rlhf and evaluation rwkv howto https github com hannibal046 rwkv howto possibly useful materials and tutorial for learning rwkv modeleditingpapers https github com zjunlp modeleditingpapers a paper resource list on model editing for large language models awesome llm security https github com corca ai awesome llm security a curation of awesome tools documents and projects about llm security awesome align llm human https github com garyyufei alignllmhumansurvey a collection of papers and resources about aligning large language models llms with human awesome code llm https github com huybery awesome code llm an awesome and curated list of best code llm for research awesome llm compression https github com huangowen awesome llm compression awesome llm compression research papers and tools awesome llm systems https github com amberljc llmsys paperlist awesome llm systems research papers awesome llm webapps https github com snowfort ai awesome llm webapps a collection of open source actively maintained web apps for llm applications other useful resources arize phoenix https phoenix arize com open source tool for ml observability that runs in your notebook environment monitor and fine tune llm cv and tabular models emergent mind https www emergentmind com the latest ai news curated explained by gpt 4 sharegpt https sharegpt com share your wildest chatgpt conversations with one click major llms data availability https docs google com spreadsheets d 1bmpddlzxvtclelgvpgzomtq0idp2 7v7qziprzpdhym edit gid 0 500 best ai tools https vaulted polonium 23c notion site 500 best ai tools e954b36bf688404ababf74a13f98d126 cohere summarize beta https txt cohere ai summarize beta introducing cohere summarize beta a new endpoint for text summarization chatgpt wrapper https github com mmabrouk chatgpt wrapper chatgpt wrapper is an open source unofficial python api and cli that lets you interact with chatgpt open evals https github com open evals evals a framework extend openai s evals https github com openai evals for different language model cursor https www cursor so write edit and chat about your code with a powerful ai autogpt https github com significant gravitas auto gpt an experimental open source application showcasing the capabilities of the gpt 4 language model openagi https github com agiresearch openagi when llm meets domain experts hugginggpt https github com microsoft jarvis solving ai tasks with chatgpt and its friends in huggingface easyedit https github com zjunlp easyedit an easy to use framework to edit large language models chatgpt shroud https github com guyshilo chatgpt shroud a chrome extension for openai s chatgpt enhancing user privacy by enabling easy hiding and unhiding of chat history ideal for privacy during screen shares contributing this is an active repository and your contributions are always welcome i will keep some pull requests open if i m not sure if they are awesome for llm you could vote for them by adding to them if you have any question about this opinionated list do not hesitate to contact me chengxin1998 stu pku edu cn 1 this is not legal advice please contact the original authors of the models for more information | ai |
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deep-in-fe | build status https travis ci org tjx666 deep in fe svg branch master https travis ci org tjx666 deep in fe codecov https codecov io gh tjx666 deep in fe branch master graph badge svg https codecov io gh tjx666 deep in fe devdependencies status https david dm org tjx666 deep in fe dev status svg https david dm org tjx666 deep in fe type dev known vulnerabilities https snyk io test github tjx666 deep in fe badge svg targetfile package json https snyk io test github tjx666 deep in fe targetfile package json percentage of issues still open https isitmaintained com badge open tjx666 deep in fe svg http isitmaintained com project tjx666 deep in fe javascript 1 promise a promise https github com tjx666 deep in fe tree master src promise then catch finally resolve reject all race allsettled 2 async await https github com tjx666 deep in fe tree master src co 3 node eventemitter https github com tjx666 deep in fe tree master src eventemitter on off once prependlistener setmaxlisteners 4 instanceof https github com tjx666 deep in fe tree master src instanceof 5 new https github com tjx666 deep in fe tree master src new 6 javascript https github com tjx666 deep in fe tree master src is isnumber isstring isboolean isobject isobjectlike isplainobject isarguments 7 reduce https github com tjx666 deep in fe tree master src powerfulreduce reduce map reduce filter url querystring promise 8 https github com tjx666 deep in fe tree master src debounce 9 https github com tjx666 deep in fe tree master src throttle 10 https github com tjx666 deep in fe tree master src extends es6 class 11 https github com tjx666 deep in fe tree master src eventdelegation 12 call apply bind https github com tjx666 deep in fe tree master src callapplybind 13 https github com tjx666 deep in fe tree master src flat reduce tostring splice flatdepth 14 https github com tjx666 deep in fe tree master src privateproperty 15 redux https github com tjx666 deep in fe tree master src redux createstore compose 16 https github com tjx666 deep in fe tree master src clonedeep 17 ajax https github com tjx666 deep in fe tree master src ajax ajax jsonp 18 null https github com tjx666 deep in fe tree master src safeget lodash get proxy 19 koa https github com tjx666 deep in fe tree master src koamiddleware 20 curry https github com tjx666 deep in fe tree master src curry 21 scheduler https github com tjx666 deep in fe tree master src scheduler 22 https github com tjx666 deep in fe tree master src scheduler once memorize | frontend mystery | front_end |
Faalis | faalis gem version https img shields io gem v faalis svg style flat http badge fury io rb faalis build status https travis ci org yellowen faalis svg branch master https travis ci org yellowen faalis downloads https img shields io gem dt faalis svg http rubygems org gems faalis test coverage https codeclimate com github yellowen faalis badges coverage svg https codeclimate com github yellowen faalis coverage code climate https codeclimate com github yellowen faalis badges gpa svg https codeclimate com github yellowen faalis dependency status https gemnasium com yellowen faalis svg https gemnasium com yellowen faalis lisence https img shields io github license yellowen faalis svg http www gnu org licenses old licenses gpl 2 0 en html important note we are planning for v3 0 0 of faalis this version would be a totally different version if you like to know more or even join this process please visit our website http faalis io and join th gitter chat faalis is well tuned platform to create web applications as fast as possible it is built on top of other quality tools and provides additional features like a robust dashboard pre baked authentication and authorization and other cool stuff installation simply add these to the end of your gemfile ruby source http rails assets org do gem rails assets sugar gem rails assets bootstrap rtl gem rails assets jquery knob gem rails assets bootstrap daterangepicker gem rails assets jquery sparkline gem rails assets jquery icheck gem rails assets admin lte end gem faalis then install your project dependencies using bundle command bash bundle install use faalis install generator to install faalis into your project by issuing the following command bash rails g faalis install add this to your config environments development rb file ruby config action mailer default url options host localhost 3000 in production host should be set to the actual host of your application perform rake db migrate db seed and enjoy faalis note you can specify the orm you d like to use in config initializers faalis rb documents it s easier to starts with faalis guides http guides faalis io but you can checkout the automated rubydoc http rubydoc info gems faalis docs contributing 1 fork it 2 create your feature branch git checkout b my new feature 3 commit your changes git commit am add some feature 4 push to the branch git push origin my new feature 5 create new pull request also you can join us in our irc channel faalis on freenode it will redirect you to 5hit p credit yellowen http www yellowen com images logo png faalis is maintained and funded by yellowen whenever a code snippet is borrowed or inspired by existing code we try to credit the original developer designer in our source code let us know if you think we have forgotten to do this and we will do our best to locate the problem and fix it in a timely manner license faalis is copyright 2013 2017 yellowen it is free software and may be redistributed under the terms specified in the license file | ruby rails dashboard control-panel | front_end |
stephen-kithya | stephen kithya information technology | server |
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plusstrap | plusstrap 0 1 0 http xbreaker github com plusstrap plusstrap provides simple and flexible html css and javascript for popular user interface components and interactions in other words it s a front end toolkit for faster more beautiful web development it s forked from twitter bootstrap http twitter github com bootstrap and maintained by xbreaker http twitter com xbreaker to get started checkout http xbreaker github com plusstrap quick start clone the repo git clone git github com xbreaker plusstrap git or download the latest release https github com xbreaker plusstrap zipball master versioning for transparency and insight into our release cycle and for striving to maintain backward compatibility plusstrap will be maintained under the semantic versioning guidelines as much as possible releases will be numbered with the following format major minor patch and constructed with the following guidelines breaking backward compatibility bumps the major and resets the minor and patch new additions without breaking backward compatibility bumps the minor and resets the patch bug fixes and misc changes bumps the patch for more information on semver please visit http semver org bug tracker have a bug please create an issue here on github also when filing please make sure you re familiar with necolas s guidelines https github com necolas issue guidelines thanks 3 https github com xbreaker plusstrap issues twitter account keep up to date on announcements and more by following plusstrap on twitter xbreaker http twitter com xbreaker contributing please make all pull requests against wip branches also if your unit test contains javascript patches or features you must include relevant unit tests thanks authors ayrat belyaev http twitter com xbreaker http github com xbreaker twitter bootstrap authors mark otto http twitter com mdo http github com markdotto jacob thornton http twitter com fat http github com fat copyright and license licensed under the apache license version 2 0 the license you may not use this work except in compliance with the license you may obtain a copy of the license in the license file or at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | css bootstrap | front_end |
bitpredict | bitpredict summary this project aims to make high frequency bitcoin price predictions from market microstructure data the dataset is a series of one second snapshots of open buy and sell orders on the bitfinex exchange combined with a record of executed transactions data collection began 08 20 2015 a number of engineered features are used to train a gradient boosting model and a theoretical trading strategy is simulated on historical and live data target the target for prediction is the midpoint price 30 seconds in the future the midpoint price is the average of the best bid price and the best ask price features width this is the difference between the best bid price and best ask price power imbalance this is a measure of imbalance between buy and sell orders for each order a weight is calculated as the inverse distance to the current midpoint price raised to a power total weighted sell order volume is then subtracted from total weighted buy order volume powers of 2 4 and 8 are used to create three separate features power adjusted price this is similar to power imbalance but the weighted distance to the current midpoint price not inverted is used for a weighted average of prices the percent change from the current midpoint price to the weighted average is then calculated powers of 2 4 and 8 are used to create three separate features trade count this is the number of trades in the previous x seconds offsets of 30 60 120 and 180 are used to create four separate features trade average this is the percent change from the current midpoint price to the average of trade prices in the previous x seconds offsets of 30 60 120 and 180 are used to create four separate features aggressor this is measure of whether buyers or sellers were more aggressive in the previous x seconds a buy aggressor is calculated as a trade where the buy order was more recent than the sell order a sell aggressor is the reverse the total volume created by sell aggressors is subtracted from the total volume created by buy aggressors offsets of 30 60 120 and 180 are used to create four separate features trend this is the linear trend in trade prices over the previous x seconds offsets of 30 60 120 and 180 are used to create four separate features model the above features are used to train a gradient boosting model the model is validated using a shifting 100 000 second window where test data always occurs after training data the length of training data accumulates with each successive iteration average out of sample r squared is used as an evaluation metric with four weeks of data an out of sample r squared of 0 0846 is achieved backtest results a theoretical trading strategy is implemented to visualize model performance at any model prediction above a threshold a simulated position is initiated and held for 30 seconds with only one position allowed at a time theoretical execution is done at the midpoint price without transaction costs the results at different thresholds can be seen below three weeks of data are used for training with one week of data used for theoretical trading strategy with a 0 01 trading threshold images strategy01 png strategy with a 0 05 trading threshold images strategy05 png live results the model was run on live data and theoretical results were displayed on a web app performance with a 0 01 trading threshold can be seen below live strategy with a 0 01 trading threshold images live results png | ai |
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crm-mobilesdk-tool-svcutil | dynamics crm mobile sdk early bound types generator the mobilesdkgen tool reads entity and option set metadata from your dynamics crm organization including custom and customized entities and then creates output files containing early bound type classes for each crm entity and option set that was read you add the output files to your mobile app project to enable access to these crm specific classes this mobilesdkgen tool can create both java android and objective c ios output files since this tool is written as a net application you will need a development computer running net framework 4 0 or later to build the tool from the provided source code this tool is similar to the crmsvcutil tool provided in the net version of the crm sdk for more information on the crmsvcutil tool and generating early bound types refer to the topic create early bound entity classes with the code generation tool crmsvcutil exe https msdn microsoft com en us library gg327844 aspx building the tool when building the tool from the provided source code several nuget http nuget org packages will be downloaded as part of the build process you will need internet access on your development computer for this to happen or the build will fail to build the tool follow these steps 1 open the provided solution file named mobilesdkgen mobilesdkgen sln in visual studio 2 choose build build solution running the tool after building the tool the mobilesdkgen exe executable will reside in your bin debug or bin release folder running the tool without any command line arguments displays usage information below is the command syntax mobilesdkgen exe c url org root url username username password password o output file folder name j i m path to xml model file for example mobilesdkgen c url https myorg crm dynamics com username un password pw o mymodel j supported command arguments below are the supported command line arguments argument description c name connection string description the connection string to be used to connect to the crm organization boolean no required yes o name output file description the path or name for the code file s to be generated should not include a file suffix boolean no required yes m name model file description the path or file name for the xml model file used to select entities to generate boolean no required no j name generate java description generate the code in java boolean yes required no i name generate objective c description generate the code in objective c boolean yes required no the o command argument is the name of the file or folder you want the output to go to if you are generating objective c then all classes are written into one file with the specified name if you are generating java then the output folder with the specified name will contain one java file for each option set and entity read restricting output to an entity set the m argument is used to specify an xml model file that identifies the entities and attributes that you do want in the generated output the following xml code examples shows how to generate output for the complete account entity and only the contactid and fullname attributes of the contact entity all global option sets are also included in the output xml xml version 1 0 encoding utf 8 model xmlns xsi http www w3 org 2001 xmlschema instance xsi nonamespaceschemalocation entities xsd includeallentities false includeallglobaloptionsets true entities entity name account entity name contact attributes attribute name contactid attribute name fullname attributes entity entities model the schema for the xml model file is shown below this schema is included in an xsd file named modelfileschema xsd that is provided in this sdk xml xml version 1 0 encoding utf 8 xs schema xmlns xsi http www w3 org 2001 xmlschema instance attributeformdefault unqualified elementformdefault qualified xmlns xs http www w3 org 2001 xmlschema xs element name model xs complextype xs sequence xs element name entities xs complextype xs sequence xs element maxoccurs unbounded name entity xs complextype xs sequence minoccurs 0 xs element name attributes xs complextype xs sequence xs element name attribute xs complextype xs attribute name name type xs string use required xs complextype xs element xs sequence xs complextype xs element xs sequence xs attribute name name type xs string use required xs complextype xs element xs sequence xs complextype xs element xs sequence xs attribute name includeallentities type xs boolean use required xs attribute name includeallglobaloptionsets type xs boolean use required xs complextype xs element xs schema | front_end |
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